Environmental Data Platform

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Results: 532 items found
4DMED hydrological stations
Hydrological station data collected in the 4DMED projects: https://www.4dmed-hydrology.org/ Data for these stations are available for project partners only, via API using the token.

Maps

Global,4dmed_stations,features,hydrology,piezometer,river,snow,water

Adaptive Capacity indicator
Adaptive capacity indicator of the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project

Maps

Alpine region,Alps,Eurac,snow tourism destinations,vulnerability

ADO - Discharge hydrological datasets
Daily time-series of discharge stations present within the Alpine space, obtained from different providers. Is made up of four columns: id station, date, discharge and the data quality information given by its different providers. The database contains observational daily discharge data deriving from the first measurement (differs for each region) to the present, with more than 1400 stations. These data were collected from multiple data providers within the ADO study region, covering the countries Austria, France, Germany, Italy, Slovenia and Switzerland. The spanned period is 1869-2021. The missing dates were added in order to have continuous time-series.

PostgreSQL

hydrology,water lever,discharge,database,ground station,cct,Environmental monitoring facilities,Hydrography

Jan. 1, 1869, 1 a.m. Dec. 31, 2021, 1 p.m.

ADO Factsheet: Soil Moisture Anomalies (SMA)
This factsheet provides a detailed technical description of the indicator Soil Moisture Anomaly (SMA), which is implemented in the Alpine Drought Observatory (ADO), and used for detecting and monitoring agricultural drought conditions.

Other

SMA,ADO,ADO project,Soil Moisture Anomalies,ADO platform,cct,Slovenia,Italy,Europe,Austria,Hydrography,natural dynamics,natural areas, landscape, ecosystems,climate,disasters, accidents, risk,water

Jan. 1, 1979, 1 a.m. None

ADO Factsheet: Standardized Precipitation-Evapotranspiration Index (SPEI)
The Standardised Precipitation-Evapotranspiration Index (SPEI) is a multiscalar drought index based on precipitation and potential evapotranspiration. It can be used for determining the onset, duration and magnitude of drought conditions with respect to normal conditions in a variety of natural and managed ecosystems such as agricultural ecosystems, forests, rivers, etc. The SPEI shows the anomalies (deviations from the long-term average) of the observed total surface water balance for any given location and accumulation period of interest. The name of the index is usually modified to include the accumulation period. SPEI-3, for example, refers to accumulation period of three months (Vicente-Serrano and Beguería, 2022).

Other

SPEI,ADO,ADO project,Standardized Precipitation-Evapotranspiration Index,ADO platform,cct,Europe,Slovenia,Austria,Italy,Hydrography,natural dynamics,natural areas, landscape, ecosystems,climate,disasters, accidents, risk,water

Jan. 1, 1979, 1 a.m. None

ADO Factsheet: Standardized Precipitation Index (SPI)
The Standardised Precipitation Index (SPI) is the most commonly used index worldwide for detecting and characterising meteorological droughts. A meteorological drought is defined as a period with abnormal precipitation deficit, in relation to normal conditions for a region, represented as a long-term average. The SPI shows the normalisedanomalies (deviations from the long-term average) of the observed total precipitation for any given location and accumulation period of interest. The name of the index is usually modified to include the accumulation period. SPI-3, for example, refers to accumulation period of three months (JRC EDO, 2020).

Other

SPI,ADO,ADO project,Standardized Precipitation Index,ADO platform,cct,Slovenia,Italy,Europe,Austria,Hydrography,natural dynamics,natural areas, landscape, ecosystems,climate,disasters, accidents, risk,water

Jan. 1, 1979, 1 a.m. None

ADO Factsheet: Standardized Snowpack Index (SSPI)
When precipitation falls in the form of snow, water may be stored as snowpack. This decreases runoff and groundwater recharge during the winter months and contributes to the increased runoff during the spring and early summer. The Standardized SnowPack Index provides information on the relative volume of the snowpack in the catchment on a ten-daily and monthly basis compared to the period of reference (JRC EDO, 2022). The name of the index is modified to include the averaging period in days. SSPI-10, for example, refers to an average period of 10 days (JRC EDO, 2020).

Other

SSPI,ADO,ADO project,Standardized Snowpack Index,ADO platform,cct,Slovenia,Italy,Europe,Austria,Hydrography,natural dynamics,natural areas, landscape, ecosystems,climate,disasters, accidents, risk,water

Jan. 1, 1979, 1 a.m. None

ADO Factsheet: Vegetation Health Index (VHI) and Vegetation Condition Index (VCI)
This factsheet provides a technical description of the Vegetation Health Index (VHI, Kogan, 1995), which is implemented in the Alpine Drought Observatory (ADO). VHI can be used for detecting agricultural drought and assessing its severity.

Other

VHI,VCI,Vegetation Health Index,Vegetation Condition Index,ADO platform,ADO,ADO project,cct,Slovenia,Italy,Europe,Austria,Hydrography,natural dynamics,natural areas, landscape, ecosystems,climate,disasters, accidents, risk,water

Jan. 1, 1979, 1 a.m. None

ADO Hydrological boundary
The layer shows the boundary considered in the ADO project for the hydrological datasets.The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions.

Maps

Europe,ADO,ADO_boundaries,ADO_region

ADO SNOWGRID CL Validation Report
ADO Datasets from deliverable D.T2.1.1 are downscaled from ERA5 surface datasets (Hersbach et al. 2018) using quantile mapping. As ERA5 snow data cannot be downscaled in a similar manner (no snow data available from UERRA mescan surfex; Bazile et al. 2017) and its accuracy within the alpine domain was questioned, a modified version of the deterministic snow model SNOWGRID-CL (Olefs et al. 2020) was used to derive daily fields of snow depth and snow water equivalent within the scope of the ADO project (part of deliverable D.T1.2.1).

Other

SPEI,ADO,ADO project,Snowgrid,ADO platform,cct,Italy,Europe,Austria,Slovenia,Hydrography,natural dynamics,natural areas, landscape, ecosystems,climate,disasters, accidents, risk,water

Jan. 1, 1979, 1 a.m. None

ADO Soil Moisture Validation Report
When precipitation falls in the form of snow, water may be stored as snowpack. This decreases runoff and groundwater recharge during the winter months and contributes to the increased runoff during the spring and early summer. The Standardized SnowPack Index provides information on the relative volume of the snowpack in the catchment on a ten-daily and monthly basis compared to the period of reference (JRC EDO, 2022). The name of the index is modified to include the averaging period in days. SSPI-10, for example, refers to an average period of 10 days (JRC EDO, 2020).

Other

SMA,ADO,ADO project,Soil Moisture Anomalies,ADO platform,cct,Slovenia,Europe,Italy,Austria,Hydrography,natural dynamics,water,natural areas, landscape, ecosystems,climate,disasters, accidents, risk

Jan. 1, 1979, 1 a.m. None

Agordino - Valle del Cordevole: Land use/Land cover
This layer shows the land use land cover for the Transalp study area Agordino-Valle del Cordevole.

Maps

Italy,land cover,landuse,Veneto

Air Quality Utilizing Indoor AI Sensors (AQUINAS) - Input data
This research provides knowledge on the performance and limitations of low-cost environmental sensors in real usage conditions. The EQ-OX platform will be used and the collected data will be compared to those of the reference station. The main purpose of this research is to compare these data and try to correct them through the use of machine learning algorithms.

InfluxDB

air quality,low-cost sensors,temperature,humidity

Feb. 13, 2024, 1 a.m. July 11, 2025, 2 a.m.

Air Quality Utilizing Indoor AI Sensors (AQUINAS) - Postprocessing data
This research provides knowledge on the performance and limitations of low-cost environmental sensors in real usage conditions. The EQ-OX platform will be used and the collected data will be compared to those of the reference station. The main purpose of this research is to compare these data and try to correct them through the use of machine learning algorithms.

InfluxDB

air quality,low-cost sensors,temperature,humidity,machine learning,ai,edge computing

Feb. 13, 2024, 1 a.m. July 11, 2025, 2 a.m.

Air temperature - Venosta Valley
Daily averaged air temperature maps [C] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

STAC

temperature,geotop,model

Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 a.m.

Air temperature - Venosta Valley
Daily averaged air temperature maps [C] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

collection,temperature,geotop,model,No platform assigned,Land use,Land cover

Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 a.m.

Alpine space-Eusalp intersection
The Alpine Drought Observatory - ADO project is interested in discharge (only for stations with a catchment area > 1000 Km2 and currently active), groundwater (only for stations for major groundwater bodies), and major lake levels (only for major water bodies (surface > 5 km2)) data. The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions. The ADO project consortium includes 11 institutions from 6 Alpine countries with a wide range of expertise, covering meteorological and hydrological monitoring, specific knowledge on modeling, drought risk and impact assessment, as well as water governance in the different sectors. Further information about the ADO project can be found here: https://www.alpine-space.eu/projects/ado/en/about.

Maps

Europe,ADO,alp,eusalp

Alpine Space Municipalities
LAU Alpine Space

Maps

Alpine region,Alps,Eurac,snow tourism destinations,vulnerability

ALPS: Glaciers outline 2015-2020
High-resolution outline of glaciers from both Sentinel-1 and Sentinel-2 satellites over South Tyrol (years 2015 to 2020, included).

Maps

Austria,features,st_glaciers_outlines_pol_s4_20152020_eurac

Annual Mean Value photovoltaic energy - CDTE modul
Annual Mean Value of photovoltaic energy produced for a Cadmium-Tellurid module.

Maps

Italy,GeoTIFF,irradiation,photovoltaic,solar,WCS

Annual Mean Value photovoltaic energy - PCSI modul
Annual Mean Value of photovoltaic energy produced for a Polykristallines-Silizium modul.

Maps

Italy,GeoTIFF,irradiation,solar,WCS

APPLE PlantVoice Database
Project: APPLE (Fusion Grant) Dataset: Tree sensor database for water-stress assessment Partners: PlantVoice; Laimburg Sites & nodes: Terlano vineyard (Laimburg) – 5; CSS Laboratories – 4; PlantVoice orchard/fields – 10+ Monitoring unit: 1 node per tree Sensors per node: PlantVoice custom sap sensor; temperature; humidity Total nodes: 19+ Date of data collection: started form 2025.06.01 and it is ongoing

Other

APPLE project,agriculture,tree water stress,sap flow sensor,Plantvoice,Agricultural and aquaculture facilities

June 1, 2025, 2 a.m. None

Apple Trait Ontology (Crop Ontology - CO)
The Apple Trait Ontology (ATO) is a vital resource for the precise and standardized description of apple varieties, enabling a deeper understanding of their genetic, morphological, and agronomic characteristics. Apples (Malus domestica) are one of the most widely cultivated and consumed fruits globally, and their immense diversity poses challenges in terms of classification, breeding, and research. ATO serves as a comprehensive framework to overcome these challenges. ATO is designed to capture and categorize a wide range of apple traits, encompassing characteristics such as fruit shape, size, color, texture, flavor, nutritional content, disease resistance, and more. This ontology employs standardized terminology and hierarchical structures to create a common language for researchers, horticulturists, and fruit breeders. The ATO not only aids in data sharing and integration but also promotes collaboration among diverse stakeholders in the apple industry.

Other

Ontology,Apple,Variety Testing,Breeding ontology,OB-VISLY,Italy,Species distribution,Agricultural and aquaculture facilities,agriculture,biology

Aug. 23, 2022, 2 a.m. None

Archivio Tirolese -Argento Vivo
Archivio Tirolese per la documentazione e l'arte fotografica di Lienz (TAP): - Collezione Lisl Gaggl-Meirer (Paesaggio, montagna; Tirolo Orientale; 1970-1990) - Collezione Klebelsberg, Istituto di Geologia, Università di Innsbruck (Paesaggio, montagna, militari; Dolomiti; 1907-1910) - Collezione Hans Peter Falkner (Città, Lienz; ca. 1965-1985) - Collezione Foto Baptist (Paesaggio, montagna; Tirolo Orientale; 1965-1975)

Maps

Italy,features,foto,archivio,picture,archive

Jan. 1, 1890, 1 a.m. Dec. 31, 1985, 1 a.m.

Aree BIPVmeetsHistory
Mappatura delle 4 Aree di progetto del territorio di Como Risultati del progetto Interreg IT-CH "BIPV meets History" Attivita' 4 - Mappatura del potenziale solare www.bipvmeetshistory.eu

Maps

Italy,Aree,BIPV,features,integrated photovoltaic,photovoltaic,solar potential

Auf den spuren der tracks
Auf den spuren der tracks in Bletterback Park

Maps

Italy,features,park,track

A validation of ADO drought indices SPI and SPEI in Slovenia
Drought indices are essential for tracking and warning of the possible drought related effects, impacts and outcomes (WMO and GWP, 2016). The use of a specific drought index in this manner requires a prior assessment of the index performance in detecting and monitoring different types of drought. Agricultural drought affects plants and crops, sometimes resulting in extensive crop failure. As agricultural drought is particularly relevant for the Alpine region and the Slovenian case study area, we investigated the ability of SPI and SPEI to detect agricultural drought through validation against annual yield data for several crops and types of grassland, averaged to statistical regions (NUTS3 level). Prior to evaluating indices against yield data, we evaluated SPI and SPEI values calculated from ERA5 reanalyses against SPI and SPEI values calculated from ground observations at representative meteorological stations in Slovenia in order to learn whether the indices calculated in the Alpine domain are relevant on a national level.

Other

SPEI,SPI,Slovenia,ADO,ADO project,ADO platform,cct,Slovenia,Europe,Hydrography,natural dynamics,natural areas, landscape, ecosystems,climate,disasters, accidents, risk,water

Jan. 1, 1979, 1 a.m. None

bdi_settl_extents_hamlets_pol
No abstract provided

Maps

Burundi,bdi_settl_extents_hamlets_pol,features

bdi_settl_extents_small_settlement_area_pol
No abstract provided

Maps

Burundi,bdi_settl_extents_small_settlement_area_pol,features

Bdi_trans_distance_roads_meters_per_colline_mean_utm35S
No abstract provided

Maps

Burundi,GeoTIFF,WCS,Bdi_trans_distance_roads_meters_per_colline_mean_utm35S

Biotop Bletterbach
Biotope area of the Bletterbach geological Park in South Tyrol.

Maps

Italy,Biotop_Bletterbach,features

bletterbachshclucht tracks
Bletterbachshclucht Parck tracks

Maps

Italy,features,park,track

Bolzano: Forest Protective Function
Surfaces with potential forest auto- and hetero-protective function.

Maps

Italy,features,forestprotectivefunction_polygon

Bolzano: Hydrological Risk Map
Hydrological risk maps of the province of Bolzano.

Maps

Italy,features,st_hazard_plan_water_2013_apb_pol_pp

Bolzano: Landslide Hazard Level Map
Gravitational mass movement hazard level maps of the province of Bolzano.

Maps

Italy,features,urbanplan-hazardzoneplan-landslides_polygon

BoscoFontana
This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

HYPERECOS,ASI,SENTINEL-2,SENTINEL2,S2,L2A

May 1, 2021, 2 a.m. Sept. 28, 2023, 2 a.m.

BOZEN AREA: Hexagonal municipality tessellation (~250m)
Tessellation onto regular hexagonal cells of the area of Bolzano at a resolution of ~250m.

Maps

Italy,bozen,features,st_tessellation_250m_bolzano_area_pol,tessellation

BOZEN AREA: tessellated OSM drivable roads (~250m)
Drivable roads from OpenStreetMap over the area of Bozen (South Tyrol) onto an hexagonal tessellation of ~250m.

Maps

Italy,drive,features,osm,st_tran_rds_ln_s4_osm_pp_drive_250tess_bz,tessellated

BOZEN: Hexagonal municipality tessellation (~250m)
Tessellation onto regular hexagonal cells of the municipality of Bolzano at a resolution of ~250m.

Maps

Italy,bozen,features,st_tessellation_250m_municipality_bolzano_pol,tessellation

BOZEN: Voronoi diagram N.1
Partition of the the municipality of Bolzano into Voronoi regions for proper aggregation of sensitive data (created by APB Osservatorio del Lavoro).

Maps

Italy,apb,features,st_tess_voronoi_pol_s4_apb

BURUNDI: 2008 Population Census by Communes
From the third general population and housing census of Burundi made by ISTEEBU Institute in 2008.

Maps

Burundi,bdi_pop_adm2_isteebu_2019_pol,features

BURUNDI: 2021 Population estimates by Communes
Population estimation by UNFPA with Institut de Statistiques et d'Etudes Economiques du Burundi (ISTEEBU). Burundi administrative level 0-2 2021 sex and age disaggregated projections from 2008 population census statistics

Maps

Burundi,distribution,Population

BURUNDI: Admin Level 0 (International) Boundaries
The dataset represents the international boundaries of Burundi.

Maps

Burundi,bdi_adm_adm0_igebu_ocha_itos_2017_utm35s,features

BURUNDI: Admin Level 1 Boundaries
The dataset represents the provinces of Burundi.

Maps

Burundi,bdi_adm_adm1_igebu_ocha_2017_utm35s,features

BURUNDI: Admin Level 2 Boundaries
The dataset represents the communes of Burundi.

Maps

Burundi,bdi_adm_adm2_igebu_ocha_2017_utm35s,features

BURUNDI: Admin Level 2 Boundaries
Burundi Level 2 administrative boundaries.

Maps

Global,bdi_adm2_test,features

BURUNDI: Admin Level 3 Boundaries
The dataset represents the collines of Burundi.

Maps

Burundi,burundi,collines

Burundi: Buildings count (100m)
This layer shows gridded buildings with 100m resolution for Burundi. It was extracted from Gridded maps of building patterns throughout sub-Saharan Africa, version 2.0. This raster contains counts of buildings that fall within a grid cell. Each buildings was counted in the grid cell that contained the centroid of its building footprint

Maps

Burundi,buildings,burundi

BURUNDI: buildings taxonomy per commune
Taxonomy of buildings in Burundi, per each commune (IDOM).

Maps

Burundi,buildings,burundi,taxonomy

BURUNDI: buildings taxonomy per province
Taxonomy of buildings in Burundi, per each province (IDOM).

Maps

Burundi,buildings,burundi,taxonomy

BURUNDI: Cropland
Land classified as cropland over Burundi (from Copernicus Land Cover product).

Maps

Burundi,bdi_lc100m_v3_2019_cropland_copernicus_utm35s,GeoTIFF,WCS

Burundi - Dams (Aquastat)
Dam locations in Burundi extracted from Aquastat Dam database for Africa. AQUASTAT gathers detailed information about dams in each country, especially on location, height, reservoir capacity, surface area and main purpose. http://www.fao.org/aquastat/en/databases/dams

Maps

Burundi,bdi_energy_dams_aquastat_pp,features

BURUNDI: Gridded Population estimates (2019 | 100m)
Gridded population estimates from WorldPop (10.5258/SOTON/WP00682) over Burundi calibrated to match the 2019 population projections by commune by ISTEEBU/UNFPA (https://data.humdata.org/dataset/burundi-administrative-level-0-2-population-statistics-2018).

Maps

Burundi,bdi_pop_worldpop_2020_rescaled_to_2019isteebuadm2_ras_,GeoTIFF,WCS

BURUNDI: Gridded Population estimates (2021 | 100m)
Gridded population estimates from WorldPop (10.5258/SOTON/WP00682) over Burundi calibrated to match the 2021 population projections by commune by ISTEEBU/UNFPA (https://data.humdata.org/dataset/burundi-administrative-level-0-2-population-statistics-2018).

Maps

Burundi,bdi_pop_worldpop100mrescaled_ras_pp,GeoTIFF,WCS

BURUNDI: Health facilities accessibility
Accessibility to nearest health facility on drivable roads in Burundi.

Maps

Burundi,bdi_health_facilities_access,features

Burundi: Health sites
This layer contains the health sites in Burundi classified by type. The data source is the Burundi Ministry of Health.

Maps

Burundi,bdi_heal_health_sites_pnt_fosa_gps_v4_minesante_utm35s

BURUNDI: High resolution landslide susceptibility map (priority areas)
Landslide susceptibility map for the priority areas of Burundi that incorporates landslide release susceptibilities and potential runout paths.

Maps

Burundi,natural hazard,WCS,landslides,bdi_haz_landslides_ras_s5_eurac_re_prio_areas

BURUNDI: Land Cover
Land classification over Burundi (from Copernicus Land Cover product).

Maps

Burundi,copernicus,GeoTIFF,WCS

BURUNDI: Landslides susceptibility map (April)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,April,bdi_haz_landslides_ras_s4_eurac_re_april,burundi,GeoTIFF,landslides,WCS

BURUNDI: Landslides susceptibility map (August)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,August,bdi_haz_landslides_ras_s4_eurac_re_august,burundi,GeoTIFF,landslides,WCS

BURUNDI: Landslides susceptibility map (December)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,GeoTIFF,WCS,December,bdi_haz_landslides_ras_s4_eurac_re_december,burundi,landslides

BURUNDI: Landslides susceptibility map (February)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_february,burundi,February,GeoTIFF,landslides,WCS

BURUNDI: Landslides susceptibility map (January)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_january,burundi,GeoTIFF,January,landslides,WCS

BURUNDI: Landslides susceptibility map (July)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_july,burundi,GeoTIFF,July,landslides,WCS

BURUNDI: Landslides susceptibility map (June)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_june,burundi,GeoTIFF,June,landslides,WCS

BURUNDI: Landslides susceptibility map (March)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_march,burundi,GeoTIFF,landslides,March,WCS

BURUNDI: Landslides susceptibility map (May)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_may,burundi,GeoTIFF,landslides,May,WCS

BURUNDI: Landslides susceptibility map (national scale)
National-scale unclassified landslide susceptibility map for Burundi.

Maps

Burundi,burundi,landslides,natural hazards

BURUNDI: Landslides susceptibility map (November)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_november,burundi,GeoTIFF,landslides,November,WCS

BURUNDI: Landslides susceptibility map (October)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_october,burundi,GeoTIFF,landslides,October,WCS

BURUNDI: Landslides susceptibility map (September)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

Burundi,bdi_haz_landslides_ras_s4_eurac_re_september,burundi,GeoTIFF,landslides,September,WCS

BURUNDI: Level-2 Geological Units
Lithological units of Burundi (second step of aggregation).

Maps

Burundi,bdi_haz_landslides_pol_s1_eurac_pp_geounits_l2,features,geological units

BURUNDI: Level-3 Geological Units
Lithological units of Burundi (third step of aggregation).

Maps

Burundi,bdi_haz_landslides_pol_s1_eurac_pp_geounits_l3,features,geological units

BURUNDI: Level-4 Geological Units
Lithological units of Burundi (fourth step of aggregation).

Maps

Burundi,bdi_haz_landslides_pol_s1_eurac_pp_geounits_l4,features,geological units

BURUNDI: Multi-Hazard Average Annual Loss (commune level)
Average Annual Loss (AAL) measured in USDs, estimated for multiple hazards over the communes of Burundi.

Maps

Burundi,aal,bdi_risk_aal_py_s4_eurac_comm,communes,features,loss,risk

BURUNDI: Multi-Hazard Average Annual Loss (province level)
Average Annual Loss (AAL) measured in USDs, estimated for multiple hazards over the provinces of Burundi.

Maps

Burundi,aal,bdi_risk_aal_py_s3_eurac_prov,features,loss,provinces,risk

Burundi: named settlements
Geographic names of populated places in Burundi by NGA Geonet Names Server (NGA). Last updated: 12. July 2021.

Maps

Burundi,burundi,settlements

BURUNDI: OSM airports
Airports of Burundi (OSM).

Maps

Burundi,features,hotosm_bdi_airports_points

BURUNDI: OSM bridges
Bridges of Burundi (OSM). OSM Download from September 2020.

Maps

Burundi,bdi_trans_roads_bridges_osm_ln_p,features

BURUNDI: OSM drivable roads
Year-round drivable roads of Burundi from OpenStreetMap (OSM).

Maps

Burundi,bdi_drive_roads,features

BURUNDI: OSM education facilities
Education facilities of Burundi (OSM).

Maps

Burundi,features,hotosm_bdi_education_facilities_points

BURUNDI: OSM health facilities
Health facilities in Burundi (OSM).

Maps

Burundi,features,hotosm_bdi_health_facilities_points

BURUNDI: OSM intrinsic completeness by discrete classification
OpenStreetMap intrinsic complete analysis by discrete classification of its collines using terrain ruggedness and gridded population estimates as auxiliary predictors.

Maps

Africa,bdi_osm_discr_class,completeness,features,intrinsic,osm

BURUNDI: OSM roads and footways
All roads and footways of Burundi from OpenStreetMap (OSM).

Maps

Burundi,bdi_all_roads,features

BURUNDI: OSM sea ports
Sea ports of Burundi (OSM).

Maps

Burundi,features,hotosm_bdi_sea_ports_points

Burundi Population 2020
Estimated total number of people per grid-cell at a resolution of 3 arc seconds.

Maps

Global,GeoTIFF,WCS,bdi_pop_ppp_2020_UNadj_constrained_Worldpop_ras_12092020

Jan. 1, 2020, midnight Dec. 31, 2020, midnight

BURUNDI: Population by Collines
100m population distribution of Burundi by Worldpop aggregated to colline level.

Maps

Burundi,bdi_pop2020_worldpop_aggregated_collinesbcg2020,features

BURUNDI: Power grid
Electricity transmission network of Burundi (World Bank+REGIDISO). https://energydata.info/dataset/burundi-electricity-transmission-network-2007

Maps

Burundi,burundi_grid,features

BURUNDI: Power plants
Power plants in Burundi with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006). https://datacatalog.worldbank.org/dataset/burundi-power-plants

Maps

Global,bdi_powerplants,features

BURUNDI: Primary and secondary roads
This layer contains primary and secondary roads in Burundi catagorising the roads into three classes and providing information on surface and usability. The data originates from the Burundi Ministry of Health.

Maps

Burundi,bdi_trans_roads_ln_dsnisv3_minesante,features

BURUNDI: Priority Areas for landslide risk assessment
TBD

Maps

Burundi,bdi_haz_landslides_pol_s4_eurac_pp_prio_areas,features

Burundi: Protected Areas
This layer shows protected areas in Burundi according to the World Database of protected areas.

Maps

Burundi,burundi,nature,protected

BURUNDI: Roads topologic indicators
The layer contains the Betweenness Centrailty indicator computed on the edges of the OpenStreetMap (OSM) main roads (up to tertiary).

Maps

Burundi,bdi_topology_indicators_main_edges,features

BURUNDI: Roads topologic indicators
The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality.

Maps

Burundi,bdi_topology_indicators_edges,features

BURUNDI: Roads topologic indicators by province
The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality. The data has been computed separately on each province of Burundi, then merged on the same file.

Maps

Burundi,bdi_topology_indicators_edges_adm1,features

Burundi: Schools
This layer shows the location of schools in Burundi. The data originates from BCG.

Maps

Burundi,bdi_edu_ecoles_v1_pnt_bcg,features

Burundi: Settlement extents - build-up area
No abstract provided

Maps

Burundi,built-up area,burundi,settlements

Burundi: Settlements (OpenStreetMap)
This layer contains populated places extracted from OpenStreetMap 01 July 2021.

Maps

Burundi,burundi,place,settlements

BURUNDI: Terrain Ruggedness (7.5 arc-sec)
Terrain ruggedness (elevation standard deviation) over Burundi at 7.5 arc-sec (225 m) of spatial resolution. Cropped from the original GMTED2010 global topographic elevation model from USGS/NGA.

Maps

Burundi,GeoTIFF,WCS

Burundi: Touristic sites
This layers shows touristic sites in Burundi. The data was provided by BCG.

Maps

Burundi,bdi_touristic_sites_pnt_bcg_iom,features

BURUNDI: Vulnerability Indices (colline level)
Vulnerability indices over Burundi at colline level (where available).

Maps

Burundi,burundi,collines,vulnerability

BURUNDI: Vulnerability Indices (province level)
Vulnerability indices over Burundi for each province.

Maps

Burundi,burundi,provinces,vulnerability

CastelPorziano
This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

HYPERECOS,ASI,SENTINEL-2,SENTINEL2,S2,L2A

May 5, 2021, 2 a.m. Sept. 27, 2023, 2 a.m.

CDD - NUTS level 0
Cooling Degree Days at country level (NUTS level 0) is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. CDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded CDD is aggregated and subsequently presented on NUTS-0 level.

Maps

Europe,cct,CDD,Nuts0

Jan. 1, 2010, 11:31 a.m. Dec. 31, 2019, 11:31 a.m.

CDD - NUTS level 2
Cooling Degree Day (CDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level.

Maps

Europe,cct,cooling,features,Nuts2

Jan. 1, 2010, 4:20 p.m. Dec. 31, 2019, 4:20 p.m.

CE_demo_cases
The layer shows the following information about Plus Energy Buildings (PEB) demo cases: 1) context, 2) key features, 3) owner, 4) Location, 5) Technologies integration, 6) Demo community and 7) partnership.

Maps

Europe,CE_demo_cases,features

Jan. 1, 2020, 11:02 a.m. Dec. 31, 2023, 11:02 a.m.

census tracts for bolzano (2011) with exposure
Support dataset for the RETURN Bolzano pilot. case. All geospatial information described in the impact chain developed in the Bolzano pilot case to be uploaded in the RETURN Almaviva platform will be defined upon this geographical boundaries.  

Maps

South Tyrol,bolzano,RETURN

CERRA-Land sub-daily regional reanalysis data for the European Alps for 2001
The Copernicus European Regional ReAnalysis Land (CERRA-Land) dataset provides spatially and temporally consistent historical reconstructions of surface and soil variables at the same horizontal resolution as the CERRA high-resolution reanalysis.

STAC

intertwin,surface solar radiation downwards,cerra_land,precipitation

Jan. 1, 2001, 1 a.m. Dec. 31, 2001, 1 a.m.

CERRA-Land sub-daily regional reanalysis orography data for the European Alps
The Copernicus European Regional ReAnalysis Land (CERRA-Land) Orography dataset provides spatially and temporally consistent historical reconstructions of orography at the same horizontal resolution as the CERRA high-resolution reanalysis.

STAC

cerra land,orography,cerra,alps

Jan. 1, 2020, 1 a.m. Jan. 1, 2020, 1 a.m.

CERRA sub-daily regional reanalysis data for the European Alps on single levels
The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of meteorological variables in the atmosphere and at the surface.

STAC

intertwin,temperature,cerra,climate

Jan. 1, 2001, 1 a.m. Dec. 31, 2001, 1 a.m.

CERRA sub-daily regional reanalysis data for the European Alps on single levels
The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of meteorological variables in the atmosphere and at the surface.

STAC

intertwin,cerra,climate

Jan. 1, 2001, 1 a.m. Dec. 31, 2001, 1 a.m.

Cleaned EPC for residential building of Lombardia Region - Cened1.2+
Cleaned EPC for residential building of Lombardia Region - Cened1.2+

Other

None

Climate Classification - NUTS0
Climate classification in european countries. The climates were extracted by the Koppen-Geiger classification

Maps

Global,cct,climate,climate_class_nut0,features

Copernicus Land Monitoring Service - EU-DEM
EU-DEM is a digital surface model (DSM) of EEA member and cooperating countries representing the first surface as illuminated by the sensors. It is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach.

STAC

elevation, terrain

Jan. 1, 2019, 1 a.m. None

Copernicus Land Monitoring Service - EU-DEM
EU-DEM is a digital surface model (DSM) of EEA member and cooperating countries representing the first surface as illuminated by the sensors. It is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach.

Other

collection,elevation,terraini,cct,SRTM / ASTER GDEM,Land use,Land cover

June 6, 2024, 2 a.m. June 6, 2024, 2 a.m.

Copernicus Land Monitoring Service - Surface Soil Moisture indexed with Kerchunk around the original netCDFs.
Copernicus Land Monitoring Service - Surface Soil Moisture 2014-present (raster 1 km), Europe, daily – version 1

STAC

CGLS,cerra,climate

Oct. 3, 2014, 2 a.m. Aug. 17, 2024, 2 a.m.

Copernicus Senwise RoseL-like imagery, Aix-en-Provence_Pennes-Mirabeau, FR 3, 2025
The input RoseL-like imagery dataset is a copy of SAOCOM-1B SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data cover the Aix-en-Provence region, in South of France, including the Pennes-Mirabeau area, where wildfires happened in Summer 2025. Coordinates are [5.23, 43.71], [5.85, 42.93]. Data are acquired in descending (DSC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,DSC,SLC,LEVEL 1A,DUAL POLARIZATION,SOUTH_FRANCE

June 25, 2025, 7:40 p.m. Sept. 29, 2025, 7:41 p.m.

Copernicus Senwise RoseL-like imagery, GrandLeez, BE 1, 2022
The input RoseL-like imagery dataset is a copy of SAOCOM-1A L-band SAR Stripmap dual pol (VH-VV) data collection over a sub-region of Wallon Region, Belgium, downloaded from saocom.asi.it. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given/provided here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. A geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,SLC,LEVEL 1A,DUAL POLARIZATION,ASC,BELGIUM

July 30, 2022, 7:18 a.m. Dec. 22, 2022, 12:59 a.m.

Copernicus Senwise RoseL-like imagery, Martigues, FR 1, 2025
The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data cover the Martigues area, South of France, where a wildfire happened in Summer 2025. Coordinates are [4.43, 43.44], [5.36, 42.92]. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,ASC,SLC,LEVEL 1A,DUAL POLARIZATION,SOUTH_FRANCE

June 26, 2025, 7:28 a.m. Sept. 30, 2025, 7:29 a.m.

Copernicus Senwise RoseL-like imagery - Mazia Matsch catchment, South Tyrol, IT, 2023
The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it and covering the Glorenza-Merano region, including the Mazia-Matsch catchment, in South-Tyrol, Italy. Coordinates are [10.29, 46.96], [11.27, 46.38]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,ASC,SAOCOM1-DERIVED,SLC,LEVEL 1A,DUAL POLARIZATION,SOUTH TYROL_ITALY

Aug. 1, 2023, 6:57 a.m. Dec. 7, 2023, 5:59 a.m.

Copernicus Senwise RoseL-like imagery - Mazia Matsch catchment, South Tyrol, IT, 2024
The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it and covering the Glorenza-Merano region, including the Mazia-Matsch catchment, in South-Tyrol, Italy. Coordinates are [10.33, 46.91], [11.33, 46.28]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,ASC,SAOCOM1-DERIVED,SLC,LEVEL 1A,DUAL POLARIZATION,SOUTH TYROL_ITALY

Jan. 8, 2024, 5:57 a.m. Dec. 25, 2024, 5:59 a.m.

Copernicus Senwise RoseL-like imagery - Mazia Matsch catchment, South Tyrol, IT, 2025
The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it and covering the Glorenza-Merano region, including the Mazia-Matsch catchment, in South-Tyrol, Italy. Coordinates are [10.30, 46.92], [11.29, 46.34]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,ASC,SAOCOM1-DERIVED,SLC,LEVEL 1A,DUAL POLARIZATION,SOUTH TYROL_ITALY

Jan. 26, 2025, 5:57 a.m. Nov. 10, 2025, 5:59 a.m.

Copernicus Senwise RoseL-like imagery, Vidauban, FR 2, 2024
The input RoseL-like imagery dataset is a copy of SAOCOM-1B SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Coordinates are [5.95, 43.46], [6.90, 42.88]. Data cover the Vidauban area, South of France, where a wildfire happened in Summer 2024. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,DSC,SLC,LEVEL 1A,DUAL POLARIZATION,SOUTH_FRANCE

May 3, 2024, 7:19 a.m. Sept. 24, 2024, 7:20 a.m.

Copernicus Senwise RoseL-like imagery - Walloon Region - BE 1, 2025
The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection over a sub-region of Wallon Region, Belgium, downloaded from saocom.asi.it. Coordinates are [4.51,51.04], [5.58,50.53]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,ASC,SLC,LEVEL 1A,DUAL POLARIZATION,BELGIUM

Oct. 10, 2025, 7:18 a.m. Nov. 11, 2025, 6:19 a.m.

Copernicus Senwise RoseL-like imagery, Walloon Region, BE 3, 2025
The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection over a sub-region of Wallon Region, Belgium, downloaded from saocom.asi.it. Coordinates are [4.02, 50.78], [4.73, 50.06]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in descending (DSC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,DSC,SLC,LEVEL 1A,DUAL POLARIZATION,BELGIUM

April 20, 2025, 7:54 p.m. Nov. 14, 2025, 6:56 p.m.

Copernicus Senwise RoseL-like imagery, Walloon Region, BE 4, 2025
The input RoseL-like imagery dataset is a copy of SAOCOM-1A SAR Stripmap dual pol (VH-VV) data collection over a sub-region of Wallon Region, Belgium, downloaded from saocom.asi.it. Coordinates are [5.36,50.20], [6.05,49.42]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in descending (DSC) orbit and are given here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. One geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,DSC,SLC,LEVEL 1A,DUAL POLARIZATION,BELGIUM

June 28, 2025, 7:48 p.m. Nov. 19, 2025, 6:50 p.m.

Copernicus Senwise RoseL-like imagery, Walloon Region, BE 5, 2025
The input RoseL-like imagery dataset is a copy of SAOCOM-1A L-band SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it, Acquisitions cover a sub-region of Wallon Region, Belgium, including the forest of Grand-Leez, for 2022. Coordinates are [4.52,51.00], [5.60,50.49]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given/provided here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. A geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,DSC,SLC,LEVEL 1A,DUAL POLARIZATION,BELGIUM

June 15, 2025, 7:50 p.m. Nov. 22, 2025, 6:52 p.m.

Copernicus Senwise RoseL-like imagery, Walloon Region, Tellin, BE 2, 2024
The input RoseL-like imagery dataset is a copy of SAOCOM-1A L-band SAR Stripmap dual pol (VH-VV) data collection downloaded from saocom.asi.it, Acquisitions cover a sub-region of Wallon Region, Belgium, including the forest of Grand-Leez, for 2022. Coordinates are [4.52,51.00], [5.60,50.49]. Data are in the Single Look Complex (SLC - Level-1A) format suitable for InSAR/PolSAR processing. Data are acquired in ascending (ASC) orbit and are given/provided here in the SAR acquisition geometry (slant range, azimuth coordinates). Both extent and numbers of rows and columns vary a bit from one image to the other: values given here correspond to the 1st image of the collection. A geoprojected (.tif) image out of the collection is provided for information only, for allowing to locate the relevant area.

STAC

SENWISE,ROSEL-LIKE,SAR DATA,SAOCOM1-DERIVED,ASC,SLC,LEVEL 1A,DUAL POLARIZATION,BELGIUM

March 29, 2024, 6:17 a.m. Dec. 26, 2024, 6:19 a.m.

Copernicus Surface Soil Moisture - 1km
The Soil Water Index quantifies the moisture condition at various depths in the soil. It is mainly driven by the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale areas, with the presence of vegetation adding complexity to the interpretation.

OpenEO

collection,surface soil moisture,ASCAT,Sentinel-1,ADO project,ADO,Sentinel-1 A/B; MetOp A/B,Land use,Land cover

Jan. 1, 2015, 1 a.m. April 19, 2020, 2 a.m.

CORINE Land Cover 2018 (CLC2018), Europe (Raster 100m)
The Corine Land Cover 2018 (CLC2018) dataset is one of the datasets produced within the CORINE Land Cover Program to capture land cover/ land use status for 2018. The Corine Land Cover (CLC) is an European programme, coordinated by the European Environment Agency (EEA), providing consistent information on land cover and land cover changes across Europe. CLC products are based on the photointerpretation of satellite images by the national teams of the participating countries - the EEA member or cooperating countries. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. The resulting European database is based on standard methodology and nomenclature with following base parameters: - 44 thematic classes in the hierarchical 3-level Corine nomenclature - minimum mapping unit (MMU) for status layers is 25 hectares - minimum width of linear elements is 100 metres - minimum mapping unit (MMU) for Land Cover Changes (LCC) for change layers is 5 hectares. It is provided as 100m raster data here

STAC

CORINE,Land Cover,Land Use,CLC,CLC2018,Europe,Copernicus,Land Monitoring

Jan. 1, 2018, 1 a.m. Jan. 1, 2018, 1 a.m.

Corine Land Cover (CLC) 2018
CLC2018 is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.

OpenEO

collection,Copernicus,Land,Satellite Image Interpretation,2018,Corine,Corine Land Cover,cct,No platform assigned,Land use,Land cover

Jan. 1, 2018, 1 a.m. Dec. 31, 2018, 1 a.m.

Corine Land Cover (CLC) 2018
CLC2018 is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.

STAC

Copernicus,Land,Satellite Image Interpretation,2018,Corine,Corine Land Cover,cct

Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evaporation product - Adige basin
Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 20-m spatial resolution for the period 2017-2018 over the Adige basin in Italy. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in GeoTIFF format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations. This dataset was derived by linear interpolation of a 100-m evaporation dataset produced over the Po River Basin (Bartkowiak et al. 2023, 2024) and it is intended to be a sample dataset for a higher-resolution improved version currently under development within the project RETURN.

STAC

Collection,Daily evapotranspiration,TSEB,Sentinel,RETURN project,RETURN,Adige basin,cct,Hydrography

Jan. 1, 2017, 1 p.m. Dec. 31, 2018, 1 p.m.

Daily evaporation product - Ebro basin
Evaporation (E) is the estimated flux of water evaporated from the land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over the Ebro basin in Spain. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

Collection,Daily evaporation,TSEB,Sentinel,4DMED project,4DMED,Ebro basin,cct,Hydrography

Jan. 1, 2017, 1 p.m. Dec. 31, 2021, 1 p.m.

Daily evaporation product Ebro basin
Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Ebro basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTiff (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

TSEB,EVAPORATION,SENTINEL

Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evaporation product - Herault basin
Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Hérault basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

Collection,Daily evaporation,TSEB,Sentinel,4DMED project,4DMED,Herault basin,cct,Hydrography

Jan. 1, 2017, 1 p.m. Dec. 31, 2021, 1 p.m.

Daily evaporation product Herault basin
Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Herault basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTiff (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

TSEB,EVAPORATION,SENTINEL

Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evaporation product - Medjerda basin
Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Medjerda basin in Tunisia. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

Collection,Daily evaporation,TSEB,Sentinel,4DMED project,4DMED,Medjerda basin,cct,Hydrography

Jan. 1, 2017, 1 p.m. Dec. 31, 2021, 1 p.m.

Daily evaporation product Medjerda basin
Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Medjerda basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTiff (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

TSEB,EVAPORATION,SENTINEL

Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily evaporation product - Po basin
Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Po basin in Italy. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

Collection,Daily evaporation,TSEB,Sentinel,4DMED project,4DMED,Po basin,cct,Hydrography

Jan. 1, 2017, 1 p.m. Dec. 31, 2021, 1 p.m.

Daily evaporation product Po basin
Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Po basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTiff (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

STAC

TSEB,EVAPORATION,SENTINEL

Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily Height of Snow for the European Alps
S3M-Alps is a 500-m reanalysis of Snow Water Equivalent and Height of Snow for the European Alps. The product is driven by ERA5-Land meteorological reanalysis downscaled to 500 m using the MicroMet statistical–dynamical method (Liston and Elder, 2006). Downscaled meteorological fields are used to force the physically-based S3M snow model (Avanzi et al., 2022).

STAC

HS,S3M,SNOW,ALPS,REANALYSIS,A-DROP

Sept. 1, 1950, 1 a.m. Dec. 31, 2025, 1 a.m.

Daily meteorological records - Climate Data Base
The Climate Database (CDB) contains meteorological time series of daily temperature (maximum, minimum and mean) and daily total precipitation for more than 250 station sites in Trentino – South Tyrol region. The data were collected from the regional meteorological networks of Bolzano and Trento Provinces and include open access records from several close sites in Austria. The spanned period is 1950 – 2021. Note that mean temperature is defined by averaging minimum and maximum values in all cases. The CDB was built in the framework of the Use-Case number 8 of the DPS4ESLAB project.

PostgreSQL

meteo,precipitation,temperature,cct,ground station,Meteorological geographical features

Jan. 1, 1946, 1 a.m. Dec. 31, 2022, 1 p.m.

Daily precipitation product
Daily precipitation (P) data at 1 km spatial resolution - Data are obtained by downscaling CPC and GPM-LR data to 1 km spatial resolution using CHELSA climatology and merging them through a triple collocation technique on CPC GPM and ERA5

STAC

PRECIPITATION,CPC,GPM,DOWNSCALING,CHELSA

Jan. 1, 2000, 1 a.m. Feb. 28, 2022, 1 a.m.

Daily Snow Water Equivalent for the European Alps
S3M-Alps is a 500-m reanalysis of Snow Water Equivalent for the European Alps. The product is driven by ERA5-Land meteorological reanalysis downscaled to 500 m using the MicroMet statistical–dynamical method (Liston and Elder, 2006). Downscaled meteorological fields are used to force the physically-based S3M snow model (Avanzi et al., 2022).

STAC

SWE,S3M,SNOW,ALPS,REANALYSIS,A-DROP

Sept. 1, 1950, 1 a.m. Dec. 31, 2025, 1 a.m.

Daily Surface Soil Moisture
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture,Land Surface Temperature and Vegetation Optical Depth from passive microwave data.

STAC

SURFACE SOIL MOISTURE,SMAP,AMSR2

Jan. 1, 2017, 1 p.m. Dec. 31, 2021, 1 p.m.

Daily TWSC product-Ebro
A 1km experimental dataset for the four Mediterranean basins of daily TWSC data from GRACE monthly JPL mascon and a water budget model driven by GLEAM-1k SM2RAIN_GPM_CPC and HDMA model

STAC

TWSC,PHYSICAL/STATISTICAL DOWNSCALING,GRACE

Jan. 1, 2017, 1 a.m. Dec. 31, 2021, 1 a.m.

Daily TWSC product-Po
A 1km experimental dataset for the four Mediterranean basins of daily TWSC data from GRACE monthly JPL mascon and a water budget model driven by GLEAM-1k SM2RAIN_GPM_CPC and HDMA model

STAC

TWSC,PHYSICAL/STATISTICAL DOWNSCALING,GRACE

Jan. 1, 2017, 1 a.m. Dec. 31, 2019, 1 a.m.

DinAlpConnect Project area
This data set represents the considered area between the Alps and Dinaric mountains to analyze the situation of ecological connectivity. Link to map: https://maps.eurac.edu/maps/1140/view File name: DinAlpConnect_Project_area.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,DinAlpConnect_Project_area,Dinaric Alps,features

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Continuum Suitability Index
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on land use, population pressure, protection status, fragmentation and topography. File name: DinaricAlps_CSI.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_CSI,GeoTIFF,WCS

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Ecological Conservation Areas (SACA1)
This data set shows important Ecological Conservation Areas (SACA1), bigger than 100ha, in the Dinaric Alps. These areas are expected to have a high biological value and that ecological connectivity is functioning well. Strategic Connectivity Areas (SACAs) derive from the Continuum Suitability Index. This approach is a way to display via GIS the most important sites for the overall ecological network on a macro-regional level (SACA1). Here, the most important ones were selected by expert evaluation. Filename: DinaricAlps_SACA1_Ecological_Conservation_Areas.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,DinaricAlps_SACA1_Ecological_Conservation_Areas,features

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Ecological Linkages (SACA2)
Ecological linkages are least cost paths, connecting the most important Ecological Conservation Areas (SACA1). They are part of the ecological intervention areas (SACA2). File name: DinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessment.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessment,features

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Ecological Restoration Areas / Barriers (SACA3)
Ecological Restoration Areas represent important barriers and have a low continuum suitability index (CSI). Ecological Restoration Areas (SACA3) are those ones, where ecological movements are not possible at the current stage and where it is necessary to implement restoration measures. These areas are currently the main barriers. For the calculation of these areas, all areas with a CSI of 1-4 were selected. File name: DinaricAlps_SACA3_Ecological_Barriers.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_SACA3_Ecological_Barriers,features

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Ecological stepping stones (SACA1)
Stepping stones are representing areas with a high ecological value, important for ecological linkages. They are calculated by the Continuum Suitability Index (CSI) and the Strategic Connectivity Areas, considering small and less important Ecological Conservation Areas. File name: DinaricAlps_SACA1_Ecological_Stepping_Stones.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_SACA1_Ecological_Stepping_Stones,features

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Environmental protection indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on the protection status of protected areas. File name: DinaricAlps_ENV.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_ENV,GeoTIFF,WCS

Jan. 1, 2020, 1 a.m. Jan. 1, 2020, 1 a.m.

Dinaric Alps: Fragmentation indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on the effective mesh size and the effective mesh density. File name: DinaricAlps_FRA.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_FRA,fragmentation,GeoTIFF,WCS

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Land cover indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on land cover classes. File name: DinaricAlps_LAN.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_LAN,GeoTIFF,WCS

Jan. 1, 2018, 1 a.m. Jan. 1, 2018, 1 a.m.

Dinaric Alps: Motorway barriers
This layer is showing motorway barriers with potential ecological linkages in the Dinaric Alps. File Name: DinaricAlps_SACA2_motorway_barriers.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_SACA2_motorway_barriers,features

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Population indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on population density data. File name: DinaricAlps_POP.tif Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_POP,GeoTIFF,population density,WCS

Jan. 1, 2018, 1 a.m. Jan. 1, 2018, 1 a.m.

DinaricAlps: Regional ecological corridors (SACA2)
This layer shows the designed width of ecological corridors, that connect Ecological Conservation Areas. An approximate width of 2km was designed by truncating the normalized cost-weighted distances of the corridors at 40.000 km. File name: DinaricAlps_SACA2_Reg_ecological_corridors.shp Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_SACA2_Reg_ecological_corridors,features

Jan. 1, 2019, 1 a.m. Jan. 1, 2019, 1 a.m.

Dinaric Alps: Topography indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on altitude and slope conditions. File name: DinaricAlps_TOP Project website: https://dinalpconnect.adrioninterreg.eu/

Maps

Europe,Dinaric Alps,DinaricAlps_TOP,GeoTIFF,WCS

Jan. 1, 2020, 1 a.m. Jan. 1, 2020, 1 a.m.

DrSchär mais field monitoring timeseries
Timeseries of the DrSchär field monitoring in Este (PD) during summer 2022. Dataset of the soil temperature and soil humidity collected from different type of sensors distributed among the mais field. In the field were installed different types of sensors as many TDR sensors and one capacitive sensor; they collect temperature and humidity of the soil. The data from the sensor were collected by two networks: a Zigbee-based and one LoraWAN-based network.

InfluxDB

water,humidity,soil moisture,temperature,soil,lorawan,zigbee,tdr,mais,capacitive,sensor,irrigation,Agricultural and aquaculture facilities,Environmental monitoring facilities,agriculture

June 9, 2022, 2 p.m. Aug. 10, 2022, 2 p.m.

Easttyrol: Vaia storm damage areas
No abstract provided

Maps

Austria,2021-04-20_transalp_aut-study-area_vaia_storm-damage-areas,features

Ecological Connectivity for Red Deer in South Tyrol
Dieser Datensatz zeigt ein ökologisches Netzwerkmodell für den Rothirsch in Südtirol. Es umfasst sowohl bestehende als auch potenzielle Querungsmöglichkeiten und soll eine Orientierung für die Definition von konkreten Korridoren darstellen. Auf lokaler Ebene kann das Modell von der Realität abweichen, weshalb die Nutzung durch Wildtiere vor Ort kontrolliert werden muss. Das Modell besitzt keine rechtliche Gültigkeit und ist noch keinem wissenschaftlichen Peer-Review-Prozess unterzogen worden.   Questo dataset mostra un modello di rete ecologica per il cervo in Alto Adige. Include sia i passaggi esistenti che quelli potenziali, e può essere usato come riferimento per definire corridoi concreti. A livello locale, il modello può differire dalla realtà, pertanto è necessario controllare l'utilizzo da parte della fauna selvatica in loco. Il modello non ha validità giuridica e non è stato ancora sottoposto ad alcun processo di revisione "Peer-review" scientifica.   File: Ecological_network_Red_deer_South_Tyrol.shp Bericht zur Erstellung des Modells (Englisch): https://www.datocms-assets.com/31538/1737642672-d2-3-1_project-of-ecological-network-south-tyrol.pdf  Bericht zu technischen Verbesserungsvorschlägen: https://www.datocms-assets.com/31538/1770718944-d-2-5-1_technischer-vorschlag_okologisches-netzwerk_hirsch_sudtirol.pdf  Proposta tecnica: https://www.datocms-assets.com/31538/1757008589-d-2-5-1_proposta-tecnica_rete-ecologica-per-il-cervo_alto-adige.pdf  Eurac project website: https://www.eurac.edu/en/institutes-centers/institute-for-regional-development/projects/plantoconnect 

Maps

Europe,Red Deer,South-Tyrol,Ecological Connectivity,corridor

Edifici BIPVmeetsHistory
Edifici casi studio del progetto di ricerca Interregionale "BIPV meets History" nel territorio di Como. Attivita' di Mappatura solare dell'area di progetto. Sito del progetto: www.bipvmeetshistory.eu

Maps

Italy,BIPVmeetsHistory,building,features

E_GLEAM_1km_2015-2021_D0_SSpain
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D10_SardiniaCorsica
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D11_Istria
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D12_Dalmatia
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D13_Epirus
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D14_Bulgaria
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D15_Greece
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D16_SWTurkey
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D17_Cyprus
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D18_Antioch
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D19_Cairo
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D1_ESpain
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D20_WEgypt
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D21_Cyrenaica
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D22_ELibya
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D23_WLibya
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D24_MidLibya
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D25_Tunisia
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D26_Algeria
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D27_Morocco
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D2_Ebro
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D3_BalearicIslands
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D4_Heraut
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D5_SFrance
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D6_Po
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D7_MidItaly
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D8_SItaly
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

E_GLEAM_1km_2015-2021_D9_Sicily
Daily land evaporation (mm/day) estimated from a fully satellite driven evaporation model GLEAM v3. E is calculated as a sum of interception loss, transpiration and bare soil evaporation. The latter two are based on a Priestley and Taylor equation constrained by soil and vegetation water content.

STAC

EVAPORATION,TRANSPIRATION,EVAPOTRANSPIRATION,GLEAM

Jan. 1, 2015, 1 a.m. Dec. 31, 2021, 1 a.m.

Electrical energy consumption profiles – Residential Apartment, Bolzano, Italy (2026)
Energy Metering from a residential building (1 apartment) in Bolzano, Italy. Period 2/2026 - 5/2026Experimental setup including PZEM as meter+ ESP32 as node + RPi as gateway + InfluxDB), one reading per circuit per ~1-minute cycle (4 circuits × ~60 s sampling). NOTES: a microwave oven, a coffee machine and a cooking robot is supplied by "lights" circuit. "Kitchen" circuit only supplies an oven and an induction cooktop. "Laundry" circuit serves only a washing machine and a heat pump dryer. Among other minor loads, a dishwasher and a fridge are supplied by "outlets" circuit. Columns: `ISO 8601 timestamp (YYYY-MM-DD HH:MM:SS.ssssss)`, `voltage (V rms)`,`current (A rms)`, `energy (Wh)`, `frequency (Hz)`, `pf`, `power (W)` License: CC-BY 4.0 DOI: 10.5281/zenodo.20099037 Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: C. Pozza (2026). Electrical energy consumption profiles – Residential Apartment, Bolzano, Italy (2026). Zenodo. https://doi.org/10.5281/zenodo.20099037Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

Other

None

Electricity Prices for Households
Electricity prices components for household consumers -annual data (from 2007 onwards). Annual values taken from the Eurostat dataset with a national level resolution.

Maps

Europe,Electricty,Household,price

Jan. 1, 2017, 6:53 p.m. Dec. 31, 2019, 6:53 p.m.

Elevation of South Tyrol
Elevation (Hypsometry) of South Tyrol.

Maps

South Tyrol,drone,defibrillator,cartography

April 1, 2023, 2 a.m. Sept. 30, 2023, 2 a.m.

Energy Cultures Drivers
The information provided intends to support a profiling exercise of users’ domestic energy use and how these variations are translated into different domestic energy-intensity practices across EU territories. This layer includes descriptive information on the energy demand dynamics at household level by means of taking into account the cultural-climatic aspects which characterise the EU climatic areas as part of this research.

Maps

Europe,cct,energy,energy_culutral_drivers,features,households

Jan. 1, 2020, 10:53 a.m. Dec. 31, 2023, 10:53 a.m.

Environmental Parameters
This layer contents specific Indoor Environmental Quality (IEQ) data sets provided which are rereferred to the closest possible locations of the Cultural-E demo cases. Specific locations and coordinates are included for each one in the IEQ related files that have been made available for downloading. Each geo-referred location contains IEQ related information and graphics, in the specific: 1. Reference year -.epw file-. 2. Climatic statistical data -.txt file-. Weather data plots, elaborated with Climate consultant and merged in a unique document -.pdf file-. 4. Weather data summary elaborated -.xls file-. Relevant information is provided in relation to: data source, used tool, implemented comfort tool, weather stations spec, software download links. This aims to illustrate the accuracy and "data fairness" of the data provided.

Maps

Global,cct,features,households,IEQ,weather

Jan. 1, 2020, 11:08 a.m. Dec. 31, 2020, 11:11 a.m.

EO4MULTIHAZARDS Events Database
The EO4MULTIHAZARDS Events Database provides the scientific community with reliable data and promotes comprehensive research efforts on multi-hazard events. The collected datasets come from various sources and are updated automatically or manually when new data becomes available. The following is a list of data sources: - EMDAT portal provides a REST-API for authorized users to download data. - EFFIS portal provides a standard and open Web Feature Service (WFS) for downloading datasets. - British Geological Survey collects events datasets in United Kingdom and uploads new data to the database as it becomes available. Additionally the DB contains a list of Earth Observation datasets, useful to study natural hazard events. The collected information is available for visualization, but the full dataset is not downloadable due to license restrictions on the input data. Events are accessible via the web interface without restriction, and query results are available for download. Additional information is available on the Web Interface page. For this project, we harmonized the input datasets into a common format to easily compare them. The database was created in the project EO4MULTIHAZARDS, funded by the The European Space Agency’s (ESA).

PostgreSQL

None

Jan. 1, 2000, 1 a.m. None

E-OBS daily gridded meteorological data for the European Alps from 1995 to present derived from in-situ observations
E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions.

STAC

intertwin,eobsv28,grid,precipitation

Jan. 1, 1995, 1 a.m. Dec. 31, 2010, 1 a.m.

EU climate classification (Köppen-Geiger)
The most frequently used climate classification map is that of Wladimir Köppen, presented in its latest version 1961 by Rudolf Geiger. A huge number of climate studies and subsequent publications adopted this or a former release of the Köppen-Geiger map. While the climate classification concept has been widely applied to a broad range of topics in climate and climate change research as well as in physical geography, hydrology, agriculture, biology and educational aspects, a well-documented update of the world climate classification map is still missing. Based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service, we present here a new digital Köppen-Geiger world map on climate classification for the second half of the 20th century.

Maps

Global,cct,climate,GeoTIFF,KG_climate_class_clip,WCS

EU NUTS 3
2021

Maps

Europe,Alps,Eurac,snow tourism destinations,vulnerability

Eurac Snow Cloud Removal Modis
Snow maps with clouds removed by spatial and temporal filters

OpenEO

collection,No keywords Available,Aqua, Terra,Land use,Land cover

Dec. 31, 2009, 1 p.m. Dec. 31, 2019, 1 p.m.

Eurac Snow Cloud Removal Modis
Snow maps with clouds removed by spatial and temporal filters. This product is not updated anymore. If you need the data please use this link: https://zenodo.org/records/3601891 to access them.

STAC

No keywords Available

Dec. 31, 2009, 1 p.m. Dec. 31, 2019, 1 p.m.

Evapotranspiration - Venosta valley
Daily evapotranspiration [mm/day] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

collection,evapotranspiration,geotop,model,No platform assigned,Land use,Land cover

Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 a.m.

Evapotranspiration - Venosta valley
Daily evapotranspiration [mm/day] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

STAC

evapotranspiration,geotop,model

Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 a.m.

Exposure indicator
Exposure indicator for the Vulnerability Map of Snow Toursim Destinations - BeyondSnow project

Maps

Alpine region,Alps,Eurac,snow tourism destinations,vulnerability

Factor available water capacity
USDA soil property map: available water capacity. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015. For further information visit the website European soil data centre (ESDAC).

OpenEO

collection,soil map,available water capacity,ESDAC,LUCAS,topsoil,ADO project,ADO,cct,N/A,Land use,Land cover

Factor available water capacity
USDA soil property map: available water capacity. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015. For further information visit the website European soil data centre (ESDAC).

STAC

soil map,available water capacity,ESDAC,LUCAS,topsoil,ADO project,ADO,cct

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Factor distance to water
Distance [m] is calculated at each location to the nearest lakes, water reservoirs, and rivers. Rivers were filtered to Strahler order greater than 3.

STAC

Distance to large water bodies,ADO project,ADO,cct

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Factor distance to water
Distance [m] is calculated at each location to the nearest lakes, water reservoirs, and rivers. Rivers were filtered to Strahler order greater than 3.

OpenEO

collection,Distance to large water bodies,ADO project,ADO,cct,N/A,Land use,Land cover

Factor elevation
ADO_elevation is a digital surface model (DSM). For further information visit website copernicus land monitoring service: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/eu-dem-v1.0?tab=metadata

STAC

Digital Elevation Model,Elevation,Copernicus Land,Copernicus,Alps,ADO project,ADO,cct

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Factor elevation
ADO_elevation is a digital surface model (DSM). For further information visit website copernicus land monitoring service: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/eu-dem-v1.0?tab=metadata

OpenEO

collection,Digital Elevation Model,Elevation,Copernicus Land,Copernicus,Alps,ADO project,ADO,cct,N/A,Land use,Land cover

Factor humus content
Organic Carbon Content In Topsoils In Europe (OCTOP) - ESDAC makes available the Maps of Organic carbon content (%) in the surface horizon of soils in Europe. Resolution: 1 km - Year: 2004. The result is available as a map and an explaining booklet: The Map of Organic Carbon Content In Topsoils In Europe: Version 1.2 September - 2003 (S.P.I.04.72).

STAC

soil Organic Carbon,Carbon content,ESDAC,topsoil,SOC,ADO project,ADO,cct

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Factor humus content
Organic Carbon Content In Topsoils In Europe (OCTOP) - ESDAC makes available the Maps of Organic carbon content (%) in the surface horizon of soils in Europe. Resolution: 1 km - Year: 2004. The result is available as a map and an explaining booklet: The Map of Organic Carbon Content In Topsoils In Europe: Version 1.2 September - 2003 (S.P.I.04.72).

OpenEO

collection,soil Organic Carbon,Carbon content,ESDAC,topsoil,SOC,ADO project,ADO,cct,N/A,Land use,Land cover

Factor landscape diversity
Shannon eveness index provides information on area composition and richness ranging from 0 to 1. It is calculated considering 9 Corine Land Cover classes of numeric matrices using a moving window algorithm of 5 pixels side and dividing this result by its maximum.

OpenEO

collection,landscape diversity,Shannon eveness index,ADO project,ADO,cct,N/A,Land use,Land cover

Factor landscape diversity
Shannon eveness index provides information on area composition and richness ranging from 0 to 1. It is calculated considering 9 Corine Land Cover classes of numeric matrices using a moving window algorithm of 5 pixels side and dividing this result by its maximum.

STAC

landscape diversity,Shannon eveness index,ADO project,ADO,cct

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Factor presence of irrigation infrastructure
Permanently irrigated agricultural land is based on the corine land cover 2018 (CLC) from Copernicus. It has been extracted the permanent irrigated class, which is the class 12 in the CLC raster. The output is a binary raster, whereas 1 corresponds for permanent irrigated land and 0 corresponds to not permanent irrigated land.

OpenEO

collection,permanent irrigated land,presence of irrigation infrastructure,ADO project,ADO,cct,N/A,Land use,Land cover

Factor presence of irrigation infrastructure
Permanently irrigated agricultural land is based on the corine land cover 2018 (CLC) from Copernicus. It has been extracted the permanent irrigated class, which is the class 12 in the CLC raster. The output is a binary raster, whereas 1 corresponds for permanent irrigated land and 0 corresponds to not permanent irrigated land.

STAC

permanent irrigated land,presence of irrigation infrastructure,ADO project,ADO,cct

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Factor slope
Slope derived from EU-DEM version 1.0. year: 2000. For further information visit: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/slope?tab=metadata

STAC

Slope,Copernicus,Land,Elevation,Digital Elevation Model,Copernicus Land,Pan-European,ADO project,ADO,cct

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Factor slope
Slope derived from EU-DEM version 1.0. year: 2000. For further information visit: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/slope?tab=metadata

OpenEO

collection,Slope,Copernicus,Land,Elevation,Digital Elevation Model,Copernicus Land,Pan-European,ADO project,ADO,cct,N/A,Land use,Land cover

Factor soil texture
USDA soil textural classes derived from clay, silt and sand maps. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015- Soil texture is classified into 12 classes: 1: Clay, 2: Silty-Clay, 3: Silty Clay-Loam, 4: Sandy Clay, 5: Sandy Clay-Loam, 6: Clay-Loam, 7: Silt, 8: Silt-Loam, 9: Loam, 10: Sand, 11: Loam Sand, 12: Sandy Loam. For further information visit the website European soil data centre (ESDAC).

STAC

soil texture,soil textural classes,ESDAC,LUCAS,topsoil,ADO project,ADO

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Factor soil texture
USDA soil textural classes derived from clay, silt and sand maps. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015- Soil texture is classified into 12 classes: 1: Clay, 2: Silty-Clay, 3: Silty Clay-Loam, 4: Sandy Clay, 5: Sandy Clay-Loam, 6: Clay-Loam, 7: Silt, 8: Silt-Loam, 9: Loam, 10: Sand, 11: Loam Sand, 12: Sandy Loam. For further information visit the website European soil data centre (ESDAC).

OpenEO

collection,soil texture,soil textural classes,ESDAC,LUCAS,topsoil,ADO project,ADO,N/A,Land use,Land cover

Farmhouses in South Tyrol
Layer to represent the position of eight case study buildings of exemplary energy efficient interventions in historic buildings. All buildings are retrofitted farm houses located in South Tyrol province.

Maps

Italy,energy refurbishment,historic buildings

Farmhouses in South Tyrol tour
Layer to represent the "tour" to eight case study buildings of exemplary energy efficient interventions in historic buildings. All buildings are retrofitted farm houses located in South Tyrol province.

Maps

Italy,historic buildings,virtual tour

Forest fires of July 2021 in Sardinia - One week after
On 24 July 2021, a large fire broke out on the Italian island of Sardinia. With strong winds, high temperatures, and dry vegetation, the blaze spread rapidly. In this image, taken about one week after, it is evident the extension area of the fire. credit: produced from ESA remote sensing data

Maps

Italy,GeoTIFF,fire,WCS

July 30, 2021, 12:15 p.m. July 30, 2021, 12:15 p.m.

Forest fires of July 2021 in Sardinia - Two days before
On 24 July 2021, a large fire broke out on the Italian island of Sardinia. With strong winds, high temperatures, and dry vegetation, the blaze spread rapidly. In this image, taken about two days before of the event, is it possible to see the destroyed vegetated area. credit: produced from ESA remote sensing data

Maps

Italy,GeoTIFF,WCS

July 22, 2021, 12:10 p.m. July 22, 2021, 12:10 p.m.

Fragsburg_rgb_flight1_3035
UAV orthophoto of apple orchard maintained by Laimburg Research Centre near Fragsburg for precision mapping of Apple Proliferation

Maps

Italy,Fragsburg_rgb_flight1_3035,GeoTIFF,WCS

Gas Prices for Household
Layer about Gas prices components for household consumers - annual data, derived by Eurostat datasets at country level.

Maps

Europe,features,gas,price

Jan. 1, 2017, 4 p.m. Dec. 31, 2019, 4 p.m.

greening_2019
Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

South Tyrol

Jan. 1, 2019, 11:27 a.m. Dec. 31, 2019, 11:12 a.m.

greening_2020
Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

South Tyrol

Jan. 1, 2020, 1 a.m. Dec. 31, 2020, 11:12 a.m.

greening_2021
Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

South Tyrol

Jan. 1, 2021, 11:11 a.m. Dec. 31, 2021, 11:12 a.m.

greening_2022
Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

South Tyrol

Jan. 1, 2022, 11:11 a.m. Dec. 31, 2022, 11:12 a.m.

greening_2023
Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

South Tyrol

Jan. 1, 2023, 11:11 a.m. Dec. 31, 2023, 11:34 a.m.

greening_2024
Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data.

Maps

South Tyrol

Jan. 1, 2024, 11:11 a.m. Dec. 31, 2024, 11:12 a.m.

Gross Domestic Product (GDP) - OLD
Layer about Gross Domestic Product prices components for household consumers - annual data, derived by Eurostat datasets at country level.

Maps

Europe,features,GDP

Jan. 1, 2010, 12:04 p.m. Dec. 31, 2019, 12:04 p.m.

Habitat suitability index for red deer in South Tyrol
The Habitat Suitability Index for red deer in South Tyrol was created to model the ecological network for this species. The habitat suitability was resampled to a cell size of 20 m by the bilinear method in ArcGIS. The area of investigation is the administrative boundary of South Tyrol with a 15 km buffer.

Maps

Europe,South Tyrol,Red deer

Harmful pollutants and microclimatic parameters from autonomous low-cost sensors deployed in the city center of Bolzano, Italy
O3, NO2, PM1, PM2.5, PM10, temperature, relative humidity, atmospheric pressure and solar radiation low-cost sensors have been co-located with the certified air quality monitoring station of Bolzano - Italy (UTM 46°49′44″ N, 11°34′24″ E, 262 m a.s.l.) managed by the local environmental agency (APPA). The filed campaign is aimed at comparing the low-cost sensors' timeseries to high-resolution instruments, which comply with the protocol for standardized acquisition released by the European Environmental Agency. The core unit of the low-cost sensors acquisition system is a Raspberry Pi 4, which couples to the sensors via the I2C bus. Specifically, the low-cost sensors involved in the campaign are: O3 (Alphasense OX-A431), NO2 (Alphasense NO2-A431) and PM1, 2.5, 10 (Alphasense OPC-N3), Tin/RHin (Sensirion SHT31), Tout/RHout (Galtech PM15PS), atmospheric pressure (Bosch Sensortech BMP388), and SR (Apogee SP 420 Smart).

InfluxDB

air,pollution,relative humidity,temperature,solar radiation,O3,NO2,PM,atmospheric pressure,low cost sensor,cct,Environmental monitoring facilities,research,pollution,air,radiations,urban environment, urban stress

June 9, 2022, 2 p.m. Aug. 10, 2022, 2 p.m.

HDD - NUTS level 0
Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-0 level.

Maps

Europe,cct,energy,HDD,Nuts0

Jan. 1, 2010, 11:26 a.m. Dec. 31, 2019, 11:26 a.m.

HDD - NUTS level 2
Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level.

Maps

Europe,cct,HDD,NUT,temp

Jan. 1, 2010, 11:27 a.m. Dec. 31, 2019, 11:27 a.m.

HEU MODERATE Building Stock Data
The HEU MODERATE Building Stock Data provides information regarding the building stock for all EU27 member states at the national level (i.e., NUTS 0) considering 2020 as the reference year. Regarding the Service Sector, the data distinguishes the following subsectors: single-family houses, multifamily houses, and apartment blocks. Regarding the Service Sector, the data distinguishes the following subsectors: offices, trade, education, health, hotels and restaurants, and other non-residential buildings. Moreover, for each subsector, the data distinguishes the following construction periods: before 1945, 1945-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2010, and 2011-2020. For each building stock subsector and construction period, the data provide information regarding total values at the national level for: - Number of buildings - Number of dwellings - Number of dwellings according to ownership (i.e., owner occupied, rented, social housing) - Number of dwellings according to occupation (i.e., occupied, vacant, secondary houses) - Total constructed area - Total heated area - Total cooled area - Total final energy consumption for space heating and domestic hot water - Total final energy consumption for space cooling Moreover, the following average values for single building characteristics are provided: - Number of floors - Volume-to-surface ratio - Vertical area - Ground area - Window surface - U-values for the different building elements (roof, walls, windows, and floors) - Useful energy demand (ued) differentiating between space heating, domestic hot water, and space cooling - Final energy consumption (fed) differentiating between space heating, domestic hot water, and space cooling Finally, the data provide information about the prevalence of: - Building materials and methodology for the different building elements (roof, walls, windows, and floors) - Different systems used for space heating, domestic hot water, and space cooling The data is provided as a `csv` file (long format with all details and data source) and as an excel file (wide format with separate sheets for each country). Data and a complete description of the available fields can be found at https://github.com/MODERATE-Project/building-stock-analysis/tree/main/T3.2-static-analysis The dataset was obtained by combining information from European and national resources and the review of scientific literature. Data gaps were subsequently filled via statistical modeling.

Other

Building Stock,EU27,Residential Sector,Service Sector,Energy Consumption,H&C Systems,Construction Materials,cct,Buildings,energy,building

Jan. 1, 2020, 1 a.m. Jan. 1, 2020, 1 a.m.

High resolution climatological large ensemble for the Alpine Region
The A-DROP LE is a single model initial condition large ensemble of the Alpine Region. It is based on the Canadian Earth System Model 2 Large Ensemble (CanESM2-LE), that is dynamically downscaled with the Canadian Regional Climate Model (CRCM5) to 12 km spatial resolution (CRCM5-LE) over the European domain (Leduc et al., 2019). It consists of 5 climate variables (air temperature, dew point temperature, precipitation, incoming shortwave radiation and wind speed), that are bias-corrected via MBCn (Cannon, 2018) and statistically downscaled to a spatial resolution of 500 m using the method of Marke, 2008. The temporal resolution is sub-daily (every three hours).

STAC

CLIMATE MODEL,ALPS,LARGE ENSEMBLE,CLIMATE,MODEL,HIGH RESOLUTION,SUB-DAILY,A_DROP

Jan. 1, 1991, 1 a.m. Dec. 31, 2098, 10 p.m.

Household Cooking Practices
This Layer shows the share of fuels in the final energy consumption in the residential sector for coocking. The Frequency is annual.

Maps

Europe,cooking,energy,households

Jan. 1, 2018, 12:38 p.m. Dec. 31, 2018, 12:38 p.m.

Households Electricity consumption
Layer about Electricity Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset.

Maps

Europe,Electricty,features,households

Jan. 1, 2007, 11:44 a.m. Dec. 31, 2018, 11:44 a.m.

Households Gas Consumption
Layer about Gas Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset.

Maps

Europe,features,gas,houshold

Jan. 1, 2007, 11:41 a.m. Dec. 31, 2018, 11:41 a.m.

Household Space Cooling
The Layers shows the share of final energy consumption in the residential sector for space cooling. The frequency of data is annual.

Maps

Europe,cct,cooling,houshold,Space

Jan. 1, 2018, 12:42 p.m. Dec. 31, 2018, 12:42 p.m.

Household Space Heating
The Layers shows the share of fuels in the final energy consumption in the residential sector for space heating. The frequency of data is annual.

Maps

Europe,cct,heating,Household,Space

Jan. 1, 2018, 12:44 p.m. Dec. 31, 2018, 12:44 p.m.

Hunsruck_Hochwald_national_park
This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

HYPERECOS,ASI,SENTINEL-2,SENTINEL2,S2,L2A

May 2, 2021, 2 a.m. Sept. 29, 2023, 2 a.m.

Hydrological Basins in Africa (Sample record, please remove!)
Major hydrological basins and their sub-basins. This dataset divides the African continent according to its hydrological characteristics. The dataset consists of the following information:- numerical code and name of the major basin (MAJ_BAS and MAJ_NAME); - area of the major basin in square km (MAJ_AREA); - numerical code and name of the sub-basin (SUB_BAS and SUB_NAME); - area of the sub-basin in square km (SUB_AREA); - numerical code of the sub-basin towards which the sub-basin flows (TO_SUBBAS) (the codes -888 and -999 have been assigned respectively to internal sub-basins and to sub-basins draining into the sea)

Maps

Africa,watersheds,river basins,water resources,hydrology,AQUASTAT,AWRD

Jan. 1, 2006, 5:29 a.m. Jan. 8, 2008, 5:29 a.m.

hydrological stations - ADO project
Hydrological stations with discharge values for ADO project

Maps

Europe,cct,discharge,features,hydro_station_ado_32632,river,water

hydro_station_wtl_ado_32632
Hydrological station with Water level values, collected and harmozied for ADO project.

Maps

Europe,ADO,cct,features,hydro_station_wtl_ado_32632,level,water

IEQ Monitoring Dataset of a Coastal Hotel in Veneto, Italy (Summer–Autumn 2013)
This dataset contains indoor environmental quality (IEQ) monitoring data collected in a hotel located on the Veneto Adriatic coast (Italy) during summer–autumn 2013. Data were collected at 10-minute intervals across 10 indoor zones (8 guest rooms on 4 floors, bar, and restaurant) and one outdoor weather station. Variables include air temperature (°C), relative humidity (%), and illuminance (lux). The dataset is intended as an open benchmark resource for testing IEQ analytics tools, including thermal comfort assessment and anomaly detection algorithms, particularly within the MODERATE platform (Horizon Europe GA 101069834). LICENSE: CC-BY 4.0 DOI: 10.5281/zenodo.20122432 Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: C. Pozza (2026). IEQ Monitoring Dataset of a Coastal Hotel in Veneto, Italy (Summer–Autumn 2013). Zenodo. https://doi.org/10.5281/zenodo.20122432Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

Other

Indoor environment,Temperature,Hotel industry,Indoor air quality

IEQ monitoring – Residential Apartment, Bolzano, Italy (2026)
IEQ monitoring – Residential Apartment, Bolzano, Italy (2026) Source: SwitchBot app weekly backup ZIP exports. Devices: SwitchBot Meter for temperature & humidity; SwitchBot Smart Radiator Thermostat for the living-room TRV. Sampling interval: ~5 minutes (median). Sensors log on state-change + periodic heartbeat; bursts of readings separated by 1–3 seconds within a polling cycle are rounded/deduped to nearest minute by the pipeline. Period: 2026-02-08 → 2026-05-02 (all seven sensors). LICENSE: CC-BY 4.0 DOI: 10.5281/zenodo.20099209 Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: C. Pozza (2026). IEQ monitoring – Residential Apartment, Bolzano, Italy (2026). Zenodo. https://doi.org/10.5281/zenodo.20099209Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

Other

None

In der Bletterbachschl track
In Der Bletterback park track

Maps

Italy,features,track

Indoor Environmental Quality – Residential Apartment, Bolzano, Italy (2023–2026)
Time-series dataset of indoor environmental quality (IEQ) measurements collected at 5-minute intervals from March 2023 to May 2026 in a south-facing living room (25 m²) of a residential apartment in Bolzano, Italy. Data were acquired using a Netatmo HomeCoach sensor and cover four years of continuous monitoring across four CSV files (one per year). Variables include: timestamp, air temperature (°C), relative humidity (%), CO₂ concentration (ppm), noise level (dB), and atmospheric pressure (hPa). The apartment is equipped with radiators for heating and an air conditioning system for cooling. The dataset is suitable for occupancy pattern analysis, thermal comfort studies, indoor air quality benchmarking, and training/validation of building energy or comfort models. LICENSE: CC-BY 4.0DOI: 10.5281/zenodo.20098783 Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: C. Pozza (2026). Indoor Environmental Quality – Residential Apartment, Bolzano, Italy (2023–2026). Zenodo. https://doi.org/10.5281/zenodo.20098783Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

Other

Building,Indoor environment,Indoor air quality

Industry, trade and education buildings in Europe
Layer to represent the position of eight case study buildings of exemplary energy efficient interventions in historic buildings. The case studies represent historic buildings with a particular use, such as for industry, trade or education.

Maps

Europe,energy refurbishment,historic buildings

Industry, trade and education buildings in Europe tour
Layer to represent the "tour" to eight case study buildings of exemplary energy efficient interventions in historic buildings. The case studies represent historic buildings with a particular use, such as for industry, trade or education.

Maps

Europe,historic buildings,virtual tour

Innovathon MODERATE 2025 - datasets
Materiales para el desarrollo del Innovathon MODERATE en Octubre 2025 en la EPI Gijón Materials for the development of the Innovathon MODERATE in October 2025 at the EPI Gijón

Other

None

INTERFACE project UAV meteorological Database
Project: INTERFACE (Euregio Science Fund) Dataset: UAV meteorological data collection Partners: UniTrento, University of Innsbruck Sites: Mezzolombardo Sensors: anemometer (wind), temperature; humidity, pressure, solar radiation Date of data collection: 2025.08.01, 2025.09.18, ongoing

Other

INTERFACE,UAV,Drone,wind measurement,anemometer,surface energy balance,eddy covariance tower,temperature,humidity,solar radiation,Atmospheric conditions,Meteorological geographical features

June 1, 2025, 2 a.m. None

LagunaOrbetello
This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

HYPERECOS,ASI,SENTINEL-2,SENTINEL2,S2,L2A

May 3, 2021, 2 a.m. Sept. 27, 2023, 2 a.m.

Land Surface Temperature - 231m 8 day mean
The Land Surface Temperature (LST) is based on MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The Land Surface Temperature is expressed in degree Celsius.

STAC

land surface temperature,lst,modis,ADO project,ADO

Jan. 1, 2001, 1 a.m. Jan. 3, 2021, 1 a.m.

LEC Location Assessment Dataset – Crevillent (Valencia Region)
This dataset supports the Local Energy Communities (LEC) Location Assessment Tool developed within the MODERATE project (Horizon Europe GA 101069834). It combines cadastral geometry, solar PV potential estimates, sociodemographic indicators, and building stock characteristics at the parcel level for the municipality of Crevillent (Valencia Region, Spain). The dataset was used to train and validate the LEC assessment analytics, enabling identification of optimal areas for the formation of local energy communities based on building morphology, roof-mounted PV capacity, energy demand proxies, and population data. Data sources include the Spanish Cadastre (Sede Electrónica del Catastro) and INE census sections. Tags/Keywords: local energy communities, LEC, solar PV, cadastral data, building stock, Valencia, Spain, energy communities, MODERATE, Horizon Europe, geo-clustering, renewable energy License: CC BY 4.0DOI: 10.5281/zenodo.20121502 Spatial coverage: Municipality of Crevillent, Province of Alicante, Valencia Region, SpainCoordinate reference system: EPSG:25830 (ETRS89 / UTM zone 30N) — geometry column; WGS84 lat/lon also present Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: E. Guillo Sansano (Grupo Enercoop) - 2026. LEC Location Assessment Dataset – Crevillent (Valencia Region). Zenodo. https://doi.org/10.5281/zenodo.20121502Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

Other

None

LiDAR-derived Forest Mapping in Mazia valley
This dataset contains forest structural and species information derived from RGB-colored LiDAR point clouds collected by Eurac Research. Using a standardized R workflow (lidR, ForestTools), the LiDAR data were processed through ground classification, height normalization, canopy height model (CHM) generation, individual tree detection, crown segmentation, point-cloud tree assignment, and RGB-based unsupervised species clustering. The result is a set of spatial layers representing tree locations, tree crowns, canopy structure, and species group labels. These data products support forest monitoring, digital twin development, ecological analysis, and modelling of forest dynamics.

STAC

Forest Mapping,Forest structure,LiDAR,UAV,CHM,Forest inventory

Oct. 22, 2025, 2 a.m. Oct. 22, 2025, 2 a.m.

Lighting and Appliances
This layer describes the energy consumption of lights and appliances on National Level for the households. The frequency of data is annual and it is connected with the Nuts0 Level.

Maps

Europe,Appliances,consumption,energy,Lighting

Jan. 1, 2018, 12:31 p.m. Dec. 31, 2018, 12:31 p.m.

Locali convenzionati
This layer shows 90 % of the locations of bars/restaurants where the Eurac lunchcard can be used. As of: 01.09.2021.

Maps

Italy,lunch,restaurant

Localities in Victoria (VMADMIN.LOCALITY_POLYGON) - Comprehensive Elements
This dataset is the definitive set of locality boundaries for the state of Victoria as defined by Local Government and registered by the Registrar of Geographic Names. The boundaries are aligned to Vicmap Property. This dataset is part of the Vicmap Admin dataset series.

Other

BOUNDARIES-Administrative,LAND-Ownership,Victoria,VIC,Data represents localities mapped since mid 2005

May 15, 2005, 2 a.m. None

Localities in Victoria (VMADMIN.LOCALITY_POLYGON) - Comprehensive Elements
This dataset is the definitive set of locality boundaries for the state of Victoria as defined by Local Government and registered by the Registrar of Geographic Names. The boundaries are aligned to Vicmap Property. This dataset is part of the Vicmap Admin dataset series.

Other

BOUNDARIES-Administrative,LAND-Ownership,Victoria,VIC,Data represents localities mapped since mid 2005

May 15, 2005, 2 a.m. None

local_policies
The information provided in this layer gives an overview of the legislation and requirements in each country and shows how they impact the spread of PEB concepts, with the help of a practical example. Different policies and related boundary conditions in each country have a great influence on the successful implementation of plus energy concepts.Therefore, national funding schemes and local policies are analysed in regard to support renewable energy generation in buildings and favour the connection with the electric grid and other district buildings (e.g. direct delivery of power to neighbour buildings, grid feed-in) as well as the local energy market (e.g. energy prices, feed-in tariff) and foreseen developments and environmental aspects.

Maps

Europe,boundary_conditions,cct,local_policies,PEB

Jan. 1, 2020, 10:48 a.m. Dec. 31, 2023, 10:48 a.m.

LSTM Representative Dataset for tile 31TFJ - Collected around the fire event in Martigues on August 4, 2020
Representative bands for the Land Surface Temperature Monitoring consolidated within the SENWISE initiative, gathered from sources as Sentinel-2, Sentinel-3 enhanced at high resolution, and simulation alogrithms

STAC

LSTM-LIKE,LST,HIGH RESOLUTION,SENWISE

July 1, 2020, 11:50 a.m. Aug. 15, 2020, 12:40 p.m.

Mean Snow Cover Duration 2041-2070 RCP8.5
Mean Snow Cover Duration according to climate predictions of the RCP 8.5 scenario from 2041 to 2070

Maps

Global,GeoTIFF,scd_2041_2070_rcp85_noglacier_16bit_3035,WCS

Jan. 1, 2041, 1 a.m. Dec. 31, 2070, 1 a.m.

MECHANICALLY VENTILATED buildings in SUMMER
In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

Europe,IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling

MECHANICALLY VENTILATED buildings in WINTER
In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

Europe,IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling

MERIT Hydro datasets
MERIT Hydro is a global hydrography datasets, developed based on the MERIT DEM and multiple inland water maps. It contains flow direction, flow accumulation, hydrologically adjusted elevations, and river channel width.

STAC

flow direction,adjusted elevation,flow accumulation,upstream drainage width,upstream drainage pixel,HAND,river channel width,MERIT Hydro,Hydrography

March 1, 2024, 1 a.m. March 1, 2024, 1 a.m.

MERIT Hydro datasets for the European Alps
MERIT Hydro is a global hydrography datasets, developed based on the MERIT DEM and multiple inland water maps. It contains flow direction, flow accumulation, hydrologically adjusted elevations, and river channel width.

STAC

flow direction,flow accumulation,river channel width,MERIT Hydro,Hydrography

March 1, 2024, 1 a.m. March 1, 2024, 1 a.m.

Meteo stations information
Layer with informations about meteorological stations for Trentino-Alto Adige region, Austria and Switzerland. The layer describe the Climate-Database of Eurac Research that contains meteorological time series of daily temperature (maximum, minimum and mean) and daily total precipitation for more than 250 station sites.

Maps

Italy,cct,climate,metadata,meteo

Microclimatic and Meteorological Data from Orchards in South Tyrol – Project INSTINCT database
The monitoring campaign carried out within the Instinct project aims to analyze the correlation between insect population dynamics and environmental conditions through the collection of microclimatic and meteorological data in various orchards in South Tyrol. Sensors have been installed at several sites: Prato allo Stelvio, Vadena (Laimburg), Naz-Sciaves, Nave San Rocco, and Sinigo. The acquisition system is based on two platforms: STM32WL55JC1, which integrates a microcontroller and LoRa module, and Raspberry Pi Zero for managing meteorological sensors such as the anemometer (ATMOS 22), pyranometer (Hukseflux SR05), radar precipitation sensor (Luff WS100), and illuminance sensor (EKO ML-020-SO). The data collected, together with trap measurements and phenological analyses, will enable the development of predictive models to understand the influence of climatic variables on the presence and evolution of insects in orchards.

Other

weather,agriculture,sustainability,sensor,monitoring,LoRaWAN,Raspberry,Atmos,weather,temperature,humidity,pyranometer,illuminance,anemometer,precipitation,Agricultural and aquaculture facilities,Environmental monitoring facilities,Meteorological geographical features

May 1, 2025, 2 a.m. None

Mobile Microclimatic Urban Monitoring
Low-cost cloud-connected position-enriched sensors for mobile monitoring of several environmental parameters have been tested in the city of Bolzano (Italy), proving their suitability in identifying the spatial variability of the local climate in relation to the urban morphology, and for highlighting the presence of urban heat island. An exploratory field campaign has been carried out in May 2021 to monitor the diurnal evolution of the microclimate conditions (Tair/RH fields). Data have been acquired performing three sessions during daytime on weekdays: at morning (i.e. 08:30-10:30), noon (i.e. 12:00-14:00), and afternoon (i.e. 16:00-18:00). The measurements have been carried out in 8 days, chosen for the stationary weather conditions (i.e. clear sky and absence of wind). The selected pathway has a length of 9 km, starting and ending at NOI Techpark, crosses the city center and reaches the northern part of the city. It is specifically designed to monitor areas of Bolzano characterized by different land use, urban morphology, and human activities.

Maps

South Tyrol,dps4eslab,features,humidity,sensor,solar,temperature,urban

MOD10A1 daily snow cover fraction for the Senales valley
This data set provides a daily snow cover fraction derived from the "MODIS/Terra Snow Cover 5-Min L2 Swath 500m" data set (DOI:10.5067/MODIS/MOD10_L2.061). The dataset is reprojected and regridded to a specific area of interest (Senales catchment). The original MODIS snow-cover products provide Normalized Difference Snow Index (NDSI) values from 0 – 100. NDSI is then converted to snow cover fraction (SCF) from 0 – 100 (Salomonson and Appel, 2004; Riggs et al., 2019). Clouds are marked as 205.

STAC

snow,SCF,MODIS,MOD10A1

Jan. 1, 2016, 1 a.m. Feb. 1, 2023, 1 a.m.

MOD16 Evapotranspiration - 500 m
Operational MODIS ET product over the Alps

STAC

evapotranspiration,energy balance,MOD16,cct

Jan. 1, 2001, 1 a.m. Dec. 27, 2019, 1 a.m.

MOD16 Evapotranspiration - 500 m
Operational MODIS ET product over the Alps

OpenEO

collection,evapotranspiration,energy balance,MOD16,cct,Aqua, Terra,Land use,Land cover

Jan. 1, 2001, 1 a.m. Dec. 27, 2019, 1 a.m.

MODERATE Solar Cadastre Dataset
This dataset contains building and rooftop characteristics for the city of Crevillent , Spain, together with estimated photovoltaic (PV) potential indicators, including suitable roof area, installable PV capacity, expected yearly electricity production, specific PV yield, and potential CO₂ emission savings. The PV potential assessment combines building footprints, annual irradiance data, terrain geometry, and shading analysis using a digital elevation model (DEM), while PV performance is derived from PVGIS and processed with PVLIB. Only technically and economically feasible rooftop areas are included. The dataset includes the following entries: Column Explanation ID Unique identifier of the building or roof section b_area Building or roof subsection area used for the calculation (in m²) p_area Total parcel or roof area associated with the building (m²) building_u Building use / building category year_cons Year of construction floors Number of floors of the building dwellings Number of residential units/apartments r_typology Residential typology thermal_ne Thermal need – Annual thermal energy demand of the building parcel, in MWh/year area_conv Roof area convenient/suitable for PV installation (m²) – this area is used for PV calculations PV_nominal Estimated installed PV peak power (kWp) PV_potential Estimated yearly PV electricity production (kWh/year) average_yi Specific PV yield (kWh/kWp/year) potentialC… Potential annual CO₂ emissions avoided (kg CO₂/year) centroid_l Latitude of building centroid centroid_1 Longitude of building centroid centroid_x X coordinate in projected coordinate system centroid_y Y coordinate in projected coordinate system Derivation of the dataset: The dataset combines building footprints with a spatial annual irradiance raster (e.g., a local solar‑cadastre / processed irradiance layer) and a digital elevation model (DEM) to capture site geometry and shading; hourly PV performance and solar geometry come from PVGIS [1] and are processed with PVLIB [2] for accurate sun positions; PVGIS hourly profiles are horizon‑corrected (using the DEM) and then scaled to match the raster’s annual totals. Finally, a set of internal technical and economic assumptions (module efficiency, performance ratio, self‑consumption/export tariffs, investment cost, discount rate, lifetime, etc.) converts energy into annual revenue and Net Present Value (NPV), and only pixels with positive NPV are considered feasible—producing per‑building feasible area, nominal capacity, annual generation (post‑shading, raster‑calibrated), specific yield, and estimated CO₂. [1] Photovoltaic Geographical Information System (PVGIS), European Commission Joint Research Centre. Available online: https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis_en [2] Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A. “pvlib python: 2023 project update.” Journal of Open Source Software, 8(92), 5994, (2023). DOI: 10.21105/joss.05994.

Other

None

MODERATE Synthetic energy consumption profiles
Synthetic dataset with 500 users hourly profile of energy metering data. The original training dataset contains both residential and non residential buildings, from 5000 users. Meaning of the columns: A+: Active energy consumed from the grid (Wh) A-: Active energy exported to the grid (Wh) R1: Consumed inductive reactive energy R2: Generated inductive reactive energy R3: Consumed cpacitive reactive energy R4: Generated capacitive reactive energy NOTE: The two variants "cpu" and "xpu" do not differ statistically from each other. They are both result obtained by MOSTLY.AI library running on a local instance, run with pytorch in the CPU (multi-thread) mode, vs a XPU pytorch run. Specific statistical metrics shows that the synthetic dataset is significantly different from original profiles. While daily and weekly patterns are maintained from the synthetization process set up, in this first release the seasonal and monthly patterns are not represented in the synthetic dataset.

Other

None

MODERATE Training dataset for synthetic load profile generator
Source: Fluvius Open Data portal Original dataset: Fluvius 2,400 digital meter profiles Description: This dataset contains 15-minute smart meter electricity readings from 2,400 anonymised residential customers connected to the Fluvius distribution network in Flanders, Belgium, covering a full calendar year. Each profile is labelled according to the presence of distributed energy technologies: photovoltaic (PV) systems, electric vehicles (EV), heat pumps (HP), or none of these. For this project, the dataset was resampled to hourly resolution and a subset of 1,300 profiles was selected, resulting in a matrix of 1,300 columns × 8,760 hourly values representing electricity consumption per building. Labels for each profile are provided in a separate indicator file. Use: The dataset was used as the basis for synthetic load profile generation within the MODERATE project.

Other

None

MODIS First Snow Day 500m
First Snow Day (FSD) represents the first date in the hydrological year with snow presence

STAC

MODIS,SNOW,MOUNTAIN ECOSYSTEMS,WATER RESOURCES

Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 a.m.

MODIS Last Snow Day 500m
Last Snow Day (LSD) represents the last date in the hydrological year with snow presence

STAC

MODIS,SNOW,MOUNTAIN ECOSYSTEMS,WATER RESOURCES

Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 a.m.

MODIS Leaf Area Index
The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies.

STAC

MODIS,terra,aqua,LAI

Jan. 1, 2020, 1 a.m. Dec. 1, 2020, 1 a.m.

MODIS Snow Cover Area 500m
The yearly snow cover area derived derived from daily MOD10A1 images

STAC

MODIS,SNOW,MOUNTAIN ECOSYSTEMS,WATER RESOURCES

Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 a.m.

MODIS Snow Cover Duration 500m
The yearly snow cover duration derived from daily MOD10A1 images indicationg the number of days with snow presence in one hydrological year

STAC

MODIS,SNOW,MOUNTAIN ECOSYSTEMS,WATER RESOURCES

Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 a.m.

MODIS SNOW map over the ALPS
The collection contains binary snow cover maps covering the entire Alpine Arc. The maps have a daily frequency and a ground resolution of 250 m. They are derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds; 4 = OCEAN WATER - pixel over sea and ocean; 5 = INLAND WATER - pixel over lakes and rivers], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].

STAC

SNOW,MODIS,SNOW MAP,ALPS

July 3, 2002, 2 p.m. None

MODIS SNOW map over the ALPS
The collection contains binary snow cover maps covering the entire Alpine Arc. The maps have a daily frequency and a ground resolution of 250 m. They are derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds; 4 = OCEAN WATER - pixel over sea and ocean; 5 = INLAND WATER - pixel over lakes and rivers], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].

OpenEO

collection,SNOW,MODIS,snow map,Alps,cct,Aqua, Terra,Land use,Land cover

July 3, 2002, 2 p.m. Sept. 22, 2024, 2 p.m.

MONALISA - SOS timeseries
Stations and timeseries of the MONALISA-SOS service. Environmental timeseries collected in the SouthTyrol province by the MONALISA project partners. Query the features and click the link to view last values collected

Maps

Italy,features,timeseries_sos

MonteBondone
This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

HYPERECOS,ASI,SENTINEL-2,SENTINEL2,S2,L2A

May 1, 2021, 2 a.m. Sept. 28, 2023, 2 a.m.

Monthly climatologies - Climate Data Base
The dataset contains the 1981 – 2010 monthly climatologies of mean, minimum and maximum temperature and total precipitation for more than 250 locations in Trentino – South Tyrol. They were derived from the observation records of the regional meteorological network after checking all series for quality and homogeneity. Climatologies (or normals) are the mean monthly values computed over a 30-years reference interval and they represent the mean local climatic conditions. The CDB was built in the framework of the Use-Case number 8 of the DPS4ESLAB project.

PostgreSQL

meteo,precipitation,temperature,cct,ground station,climatology

Jan. 1, 1946, 1 a.m. Dec. 31, 2022, 1 p.m.

Monthly damages LATEST
Mapping of the forest changes occurring in 2020, 2021, 2022, 2023 and 2024 at a monthly scale. The outputs shown here are based on the analysis of Sentinel 2 time series. The date corresponds to the first date at which a change was detected.

Maps

South Tyrol,Forest

Multi-Family Residential Building – Hourly Energy and IEQ Monitoring Time Series - Bolzano area, Italy (2006–2009)
This dataset contains three years of hourly monitored data from a multi-family residential building located in the Bolzano area, South Tyrol, Italy (46°N, Alpine climate). It covers the period from 1 July 2006 to 21 July 2009 and provides continuous time series combining energy consumption measurements with indoor environmental quality (IEQ) indicators and meteorological context variables. The dataset is released as original monitored data: no outlier removal, smoothing, or gap-filling has been applied. A number of anomalous values and atypical operational trends have been intentionally preserved. This makes the dataset well-suited for benchmarking and stress-testing anomaly detection algorithms, fault detection and diagnostics (FDD) tools, and data quality assessment pipelines in the building energy domain. Note: Temperature profiles are obtained after averaging on similar profiles from 8 selected apartments. The energy consumption is the aggregated value at building level (same apartments). Variable Description Unit Min Max Mean Electricity Hourly electrical energy consumption kWh 0.00 78.00 1.62 CO₂ concentration Indoor CO₂ concentration (occupancy/ventilation proxy) ppm 452 1908 832.71 Internal temperature Indoor air temperature °C 12.60 31.20 24.27 Thermal energy consumption Hourly thermal energy (heating/DHW) kWh 0.00 27.00 5.98 External global radiation Horizontal global solar irradiance W/m² 0.00 1035.00 151.95 Outside temperature Outdoor ambient air temperature °C −8.10 38.30 13.33 Note: min, max, and mean are computed over all 25,699 records. No missing values are present. Extreme values (e.g., electricity max 78 kWh/h; CO₂ max 1,908 ppm) reflect both real operational peaks and intentionally retained anomalous readings. Users should not treat these values as errors before understanding the anomaly detection context of this release. Related resources: Platform: https://www.moderate.cloud (interactive access, API) Documentation: https://moderate-project.github.io/moderate-docs Source code / tools: https://github.com/MODERATE-Project/ Project website: https://moderate-project.eu Grant Agreement: Horizon Europe GA 101069834 This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). You may use, share, and adapt the data for any purpose, provided you give appropriate credit. If you use this dataset, please cite: M. Castagna, C. Pozza (2026). Multi-Family Residential Building – Hourly Energy and IEQ Monitoring Time Series - Bolzano area, Italy (2006–2009). Zenodo. https://doi.org/10.5281/zenodo.20096697Project: MODERATE (Horizon Europe GA 101069834). https://www.moderate.cloud

Other

Energy consumption,Indoor air quality,Residential building

NATURALLY VENTILATED buildings in SUMMER
In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

Europe,IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling

NATURALLY VENTILATED buildings in WINTER
In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

Europe,IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling

Natural polar ecosystems
None

None

Antarctic ecosystem,Arctic ecosystem,polar ecosystem

Natural polar ecosystems
None

None

Antarctic ecosystem,Arctic ecosystem,polar ecosystem

Normalized Difference Vegetation Index - 231m 8 day Maximum Value Composite
The Normalized Difference Vegetation Index (NDVI) is based on MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The NDVI is calculated using the formula NDVI = (NIR - Red) / (NIR + Red). The NDVI expresses the vitality of vegetation. The data is provided as 8 day measures. The time series is starting from 2001. The NDVI values range from -1 - 1, whereas high values correspond to healthy vegetation.

STAC

normalized difference vegetation index,ndvi,modis,ADO project,ADO

Jan. 1, 2001, 1 a.m. Jan. 3, 2021, 1 a.m.

Normalized Difference Vegetation Index - 231m 8 day Maximum Value Composite
The Normalized Difference Vegetation Index (NDVI) is based on MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The NDVI is calculated using the formula NDVI = (NIR - Red) / (NIR + Red). The NDVI expresses the vitality of vegetation. The data is provided as 8 day measures. The time series is starting from 2001. The NDVI values range from -1 - 1, whereas high values correspond to healthy vegetation.

OpenEO

collection,normalized difference vegetation index,ndvi,modis,ADO project,ADO,Terra,Land use,Land cover

Jan. 1, 2001, 1 a.m. Jan. 3, 2021, 1 a.m.

Nuts2 alpinespace-Eusalp intersection
The Alpine Drought Observatory - ADO project is interested in discharge (only for stations with a catchment area > 1000 Km2 and currently active), groundwater (only for stations for major groundwater bodies), and major lake levels (only for major water bodies (surface > 5 km2)) data. The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions. The ADO project consortium includes 11 institutions from 6 Alpine countries with a wide range of expertise, covering meteorological and hydrological monitoring, specific knowledge on modeling, drought risk and impact assessment, as well as water governance in the different sectors. Further information about the ADO project can be found here: https://www.alpine-space.eu/projects/ado/en/about.

Maps

Europe,nuts2_alpinespaceEusalp_intersection

nuts2_simplified
NUTS region, level 2 for the EUSALP area. The border are simplified respect to the original data source to get a lighter version.

Maps

Europe,features,nuts2_simplified

Occupant Behaviour Modelling
This layer aims at creating a knowledge base related to research on Occupant Behaviour Modelling. The main Cultural-E contribution referrers to the translation into a GIS format of the provision of open-access available review tables focused on occupants' perception and behaviour in buildings (OPA)(*). In contrast to tables often found in supplementary materials, which are static, the tables used are dynamically growing with new evidence appearing in scientific literature. Authors of original research are welcome to add their published and peer-reviewed research items to these tables. (*) Schweiker M, Andersen RK, Berger C, Carlucci S, Chinazzo G, Edappilly LP, Favero M, Mahdavi A, Piselli C, Bourikas L, Hong T, Dong B, Syndicus M and Hahn J (2021) Dynamic review tables for topical reviews on occupants’ perception and behaviour in buildings. OSF. Available at: osf.io/gnvp2

Maps

Europe,building,energy,features

Ortler_Alpen_Specialkarte_Meurer-Freytag
Historical Map (1:50.000) of the Ortler Alps made by Julius Meurer (1838-1923), Gustav Freytag (?-1938) in 1884.

Maps

Europe,GeoTIFF,Ortler_Alpen_Specialkarte_Meurer-Freytag,WCS

Ospitaletto District Heating Expansion – Building Heat Demand and Network Dataset
This dataset provides georeferenced building-level annual heating demand estimates and district heating network geometry for the municipality of Ospitaletto (Brescia, Lombardy, Italy), developed to assess expansion opportunities for an existing 5th Generation District Heating (5GDHC) network. The building heat demand layer (Ospitaletto_heat.gpkg) covers 1,957 buildings with 17 attributes including footprint area, building height and volume, S/V ratio, functional use (residential/non-residential), INSPIRE-based typological class (SFH, s-MFH, l-MFH), estimated annual heating demand (MWh/year), distance to the DH network, and priority selection flags under two criteria. The DH network layer (DH_ospitaletto_network.gpkg) contains 21 pipe segments of the existing network. Both layers use the EPSG:3035 coordinate reference system. Heating demand was estimated through a data-fusion methodology combining building geometry (OpenStreetMap, cadastral data), climate inputs (Heating Degree Days, Southern Continental zone), energy consumption density benchmarks (INSPIRE for residential, Hotmaps for non-residential), and available metered consumption data for calibration. Network expansion priorities were assessed using two criteria: Criterion A selects buildings by maximizing the ratio of annual demand to distance from the network (kWh/y/m); Criterion B applies a 250 m buffer around the existing network and filters by minimum demand threshold (100 kWh/y). Produced within the MODERATE project (Horizon Europe Grant Agreement No. 101069834).

Other

None

Past Avalanche Events
Southtyrol: past avalanche events.

Maps

Italy,avalanche,events,hazard

Path simulation
Simulation for the START project of the path of 3 person in the Park site. In the future this simulation will be replaced by a near real time updated layer. You can see on the map different position at different time using the arrow of the timeline tool on the map.

Maps

Italy,features,path,person,simulation

PEB_guidelines
This layer provides exemplary national initiatives and guidelines materials for the design and experimentation for high standards of energy efficient buildings such as PEB, ZEB and NZEB. The different national initiatives referenced intends to represent the 4 EU climates which are object of study of CULTURAL-E.

Maps

Global,cct,energy,features,households,PEB_guidelines

Jan. 1, 2020, 10:59 a.m. Dec. 31, 2023, 10:59 a.m.

Perimeter of the Alpine Convention
Perimeter of the Alpine Convention

Maps

Europe,alpine convention,perimeter,2023

Perimeter of the Alpine Space
Perimeter of the Interreg Alpine Space Programme (2022)

Maps

Europe,features,alpine space

Perimeter of the European Countries
Nessun abstract inserito

Maps

Europe,europe

Physiographic Map of North and Central Eurasia (Sample record, please remove!)
Physiographic maps for the CIS and Baltic States (CIS_BS), Mongolia, China and Taiwan Province of China. Between the three regions (China, Mongolia, and CIS_BS countries) DCW boundaries were introduced. There are no DCW boundaries between Russian Federation and the rest of the new countries of the CIS_BS. The original physiographic map of China includes the Chinese border between India and China, which extends beyond the Indian border line, and the South China Sea islands (no physiographic information is present for islands in the South China Sea). The use of these country boundaries does not imply the expression of any opinion whatsoever on the part of FAO concerning the legal or constitutional states of any country, territory, or sea area, or concerning delimitation of frontiers. The Maps visualize the items LANDF, HYPSO, SLOPE that correspond to Landform, Hypsometry and Slope.

Maps

Eurasia,physiography, soil

Jan. 1, 2000, 5:29 a.m. Jan. 8, 2008, 5:29 a.m.

Pilot Working Areas
BeyondSnow Pilot Working Areas 

Maps

Alpine region,beyondsnow

PlanToConnect lcp: regional linkages and distance local linkages
This layer shows the Least Cost Path (LCP), defining regional linkages and linkages less than 2.5 km.  “A Least-Cost-Path is defined as the pathway that offers the least resistance to an animal moving from one patch to another (Cushman et al., 2013) and is represented as the linear element (least-cost pathway) that connects two patches.” (Lumia et al., 2023) File name: LCP_Regional_Linkages_and_distance_local_linkages.shp Project website: https://www.alpine-space.eu/project/plantoconnect/

Maps

Europe,Alps,eusalp,least cost path,Spatial planning

Aug. 20, 2024, 4:55 p.m. Aug. 20, 2024, 4:55 p.m.

PlanToConnect: Motorway barriers for potential ecological linkages in the Alps
This layer is showing motorway barriers with potential ecological linkages in the Alps. File Name: PlanToConnect_Motorway_barriers.shp Project website: https://www.alpine-space.eu/project/plantoconnect/

Maps

Europe,Alps,Ecological Connectivity,Motorway barriers,Urban planning

Aug. 1, 2024, 5:37 p.m. Aug. 1, 2024, 5:37 p.m.

PlanToConnect: potential ecological network EUSALP
This data set shows regional potential ecological network in EUSALP areas. Filename: PlanToConnect_Potential_ecological_network_EUSALP.shp Project website: https://www.alpine-space.eu/project/plantoconnect/

Maps

Europe,eusalp,Alps,Ecological Connectivity,Spatial planning

Aug. 2, 2024, 11:14 a.m. Aug. 2, 2024, 11:14 a.m.

Poligoni riferimento bostrico
No abstract provided

Maps

South Tyrol,features,poligoni_riferimento_bostrico

Population Density
The ratio between the annual average population and the land area. The land area concept (excluding inland waters) should be used wherever available; if not available then the total area, including inland waters (area of lakes and rivers) is used. The frequency is annual.

Maps

Europe,Density,Population

Jan. 1, 2007, 12:13 p.m. Dec. 31, 2018, 12:13 p.m.

Potential Impacts indicator
Potential impacts indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project

Maps

Alpine region,Alps,Eurac,snow tourism destinations,vulnerability

Potential UAS-AED Stations
Potential UAS-AED Network of South Tyrol.

Maps

South Tyrol,cartography,defibrillator,drone

April 1, 2023, 2 a.m. Sept. 30, 2023, 2 a.m.

Precipitation Anomalies - ERA5_QM REL_RR-1
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Precipitation Anomalies - ERA5_QM REL_RR-1
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

RR ANOMALIES,RELATIVE PRECIPITATION ANOMALIES,PRECIPITATION ANOMALIES,ERA5

Dec. 31, 1978, 1 p.m. None

Precipitation Anomalies - ERA5_QM REL_RR-12
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

RR ANOMALIES,RELATIVE PRECIPITATION ANOMALIES,PRECIPITATION ANOMALIES,ERA5

Dec. 31, 1978, 1 p.m. None

Precipitation Anomalies - ERA5_QM REL_RR-12
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Precipitation Anomalies - ERA5_QM REL_RR-2
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

RR ANOMALIES,RELATIVE PRECIPITATION ANOMALIES,PRECIPITATION ANOMALIES,ERA5

Dec. 31, 1978, 1 p.m. None

Precipitation Anomalies - ERA5_QM REL_RR-2
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Precipitation Anomalies - ERA5_QM REL_RR-3
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

RR ANOMALIES,RELATIVE PRECIPITATION ANOMALIES,PRECIPITATION ANOMALIES,ERA5

Dec. 31, 1978, 1 p.m. None

Precipitation Anomalies - ERA5_QM REL_RR-3
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Precipitation Anomalies - ERA5_QM REL_RR-6
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

OpenEO

collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Precipitation Anomalies - ERA5_QM REL_RR-6
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).

STAC

RR ANOMALIES,RELATIVE PRECIPITATION ANOMALIES,PRECIPITATION ANOMALIES,ERA5

Dec. 31, 1978, 1 p.m. None

PV classification results for the MODERATE project
The csv files contain the results of an image classifier, trained to classify images of rooftops of having or not having PV. It was used on the regions Valencia and Bolzano. The IDs are the building identifiers from Open Street Maps and the prediction classifies each building as having a PV (1) or not having a PV (0). In the respective .json file for each region the latitude and longitude are saved for each Open Street Maps ID using EPSG 3035.

Other

None

RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TPS
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.

Maps

Europe,AI4EBV,land cover,WTE

Jan. 1, 2018, 1 a.m. Dec. 31, 2018, 1 a.m.

RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TPT
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.

Maps

Europe,land cover,WTE,AI4EBV

Jan. 1, 2018, 10:05 a.m. Jan. 1, 2018, 10:05 a.m.

RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TQS
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.

Maps

Europe,AI4EBV,land cover,WTE

Jan. 1, 2018, 1 a.m. Dec. 31, 2018, 1 a.m.

RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TQT
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.

Maps

Europe,AI4EBV,land cover,WTE

Jan. 1, 2018, 1 a.m. Dec. 31, 2018, 1 a.m.

Report on existing monitoring platforms and potential data for the integration into ADO
The purpose of this report is to prepare a foundation for this working process. An overview of the state of drought observation and detection in the Alpine region has been prepared, which will also serve as basis for its upgrade or extension to the entire Alpine area. The state of the art analysis prepared by project partners (at least one per participating country) was conducted by uniform questionnaire that was circulated among ADO project partners covering four topics: drought monitoring approach, drought-information platforms, the regional & EU drought monitoring approach, and potential drought indices in ADO.

Other

SPEI,SPI,Slovenia,ADO,ADO project,ADO platform,cct,Slovenia,Europe,Hydrography,natural areas, landscape, ecosystems,natural dynamics,climate,disasters, accidents, risk,water

Jan. 1, 1979, 1 a.m. None

RT1 Surface Soil Moisture
A experimental dataset of soil-moisture retrievals at 1km effective spatial resolution for all 28 basins of the Mediterranean study-area. The data was processed via the RT1 radiative transfer model using Sentinel-1 (VV) backscatter data together with auxiliary CGLS Leaf Area Index timeseries.

STAC

SURFACE SOIL MOISTURE,SENTINEL-1,RT1,RADIATIVE TRANSFER

Jan. 1, 2017, 1 a.m. June 30, 2022, 2 a.m.

Rund um dolomiten tracks
Rund um dolomiten tracks in the Bletterback park

Maps

Italy,features,track

scd_20001001_20190930_16bit_3035
No abstract provided

Maps

Global,GeoTIFF,scd_20001001_20190930_16bit_3035,WCS

scd_2041_2070_rcp26_noglacier_16bit_3035
No abstract provided

Maps

Global,GeoTIFF,scd_2041_2070_rcp26_noglacier_16bit_3035,WCS

scd_2071_2100_rcp26_noglacier_16bit_3035
No abstract provided

Maps

Global,GeoTIFF,scd_2071_2100_rcp26_noglacier_16bit_3035,WCS

scd_2071_2100_rcp85_noglacier_16bit_3035
No abstract provided

Maps

Global,GeoTIFF,scd_2071_2100_rcp85_noglacier_16bit_3035,WCS

SCF_binary
Results for batch job j-2406072110944ac9ae59a2fb48d47a10 (SCF_Binary Map CDSE)

STAC

None

Oct. 1, 2015, 2 a.m. Sept. 30, 2022, 2 a.m.

Sciliar_Catinaccio
This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

HYPERECOS,ASI,SENTINEL-2,SENTINEL2,S2,L2A

May 1, 2021, 2 a.m. Sept. 28, 2023, 2 a.m.

Sensitivity indicator
Sensitivity indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project

Maps

Alpine region,Alps,Eurac,snow tourism destinations,vulnerability

SENTINEL2_MOSAIC_20170601_20170930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2017. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,SENTINEL2_MOSAIC_20170601_20170930_CIR,GeoTIFF,WCS

June 1, 2017, 2 a.m. Sept. 30, 2017, 2 p.m.

SENTINEL2_MOSAIC_20170601_20170930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2017. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20170601_20170930_RGB

June 1, 2017, 2 a.m. Sept. 30, 2017, 2 p.m.

SENTINEL2_MOSAIC_20180601_20180930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2018. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,GeoTIFF,SENTINEL2_MOSAIC_20180601_20180930_CIR,WCS

June 1, 2018, 2 a.m. Sept. 30, 2018, 2 p.m.

SENTINEL2_MOSAIC_20180601_20180930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2018. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,GeoTIFF,SENTINEL2_MOSAIC_20180601_20180930_RGB,WCS

June 1, 2018, 2 a.m. Sept. 30, 2018, 2 p.m.

SENTINEL2_MOSAIC_20190601_20190930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2019. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20190601_20190930_CIR

June 1, 2019, 2 a.m. Sept. 30, 2019, 2 p.m.

SENTINEL2_MOSAIC_20190601_20190930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2019. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20190601_20190930_RGB

June 1, 2019, 2 a.m. Sept. 30, 2019, 2 p.m.

SENTINEL2_MOSAIC_20200601_20200930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2020. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20200601_20200930_CIR

June 1, 2020, 2 a.m. Sept. 30, 2020, 2 p.m.

SENTINEL2_MOSAIC_20200601_20200930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2020. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20200601_20200930_RGB

June 1, 2020, 2 a.m. Sept. 30, 2020, 2 p.m.

SENTINEL2_MOSAIC_20210601_20210930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2021. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20210601_20210930_CIR

June 1, 2021, 2 a.m. Sept. 30, 2021, 2 p.m.

SENTINEL2_MOSAIC_20210601_20210930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2021. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20210601_20210930_RGB

June 1, 2021, 2 a.m. Sept. 30, 2021, 2 p.m.

SENTINEL2_MOSAIC_20220601_20220930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2022. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,SENTINEL2_MOSAIC_20220601_20220930_CIR,GeoTIFF,WCS

June 1, 2022, 2 a.m. Sept. 30, 2022, 2 p.m.

SENTINEL2_MOSAIC_20220601_20220930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2022. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20220601_20220930_RGB

June 1, 2022, 2 a.m. Sept. 30, 2022, 2 p.m.

SENTINEL2_MOSAIC_20230601_20230930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2023. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,GeoTIFF,SENTINEL2_MOSAIC_20230601_20230930_CIR,WCS

June 1, 2023, 2 a.m. Sept. 30, 2023, 2 p.m.

SENTINEL2_MOSAIC_20230601_20230930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2023. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,GeoTIFF,SENTINEL2_MOSAIC_20230601_20230930_RGB,WCS

June 1, 2023, 2 a.m. Sept. 30, 2023, 2 p.m.

SENTINEL2_MOSAIC_20240601_20240930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2024. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20240601_20240930_CIR

June 1, 2024, 2 a.m. Sept. 30, 2024, 2 p.m.

SENTINEL2_MOSAIC_20240601_20240930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2024. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20240601_20240930_RGB

June 1, 2024, 2 a.m. Sept. 30, 2024, 2 p.m.

sentinel2_mosaic_20250401_20250430_cir
Monthly cir mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

None

sentinel2_mosaic_20250401_20250430_rgb
Monthly RGB mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

sentinel2,rgb,mosaic

sentinel2_mosaic_20250501_20250531_cir
Monthly cir mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

None

sentinel2_mosaic_20250501_20250531_rgb
Monthly RGB mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

sentinel2,rgb,mosaic

sentinel2_mosaic_20250601_20250630_cir
Monthly cir mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

None

sentinel2_mosaic_20250601_20250630_rgb
Monthly RGB mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

sentinel2,rgb,mosaic

SENTINEL2_MOSAIC_20250601_20250930_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2025. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20250601_20250930_CIR

June 1, 2025, 2 a.m. Sept. 30, 2025, 2 p.m.

SENTINEL2_MOSAIC_20250601_20250930_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2025. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,SENTINEL2_MOSAIC_20250601_20250930_RGB,GeoTIFF,WCS

June 1, 2025, 2 a.m. Sept. 30, 2025, 2 p.m.

sentinel2_mosaic_20250701_20250731_cir
Monthly cir mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

None

sentinel2_mosaic_20250701_20250731_rgb
Monthly RGB mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

sentinel2,rgb,mosaic

sentinel2_mosaic_20250801_20250831_cir
Monthly cir mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

None

sentinel2_mosaic_20250801_20250831_rgb
Monthly RGB mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

None

sentinel2_mosaic_20250901_20250930_cir
Monthly cir mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

None

sentinel2_mosaic_20250901_20250930_rgb
Monthly RGB mosaic for South Tyrol with no clouds derived from Sentinel-2 image data.

Maps

sentinel2,rgb,mosaic

SENTINEL2_MOSAIC_20260501_20260611_CIR
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within May and June 2026. Displayed as false color (CIR) representation combining the near infrared, red and green bands.

Maps

Europe,GeoTIFF,WCS,SENTINEL2_MOSAIC_20260501_20260611_CIR

June 1, 2026, 2 a.m. Sept. 30, 2026, 2 p.m.

SENTINEL2_MOSAIC_20260501_20260611_RGB
Cloudfree satellite mosaic composed of Sentinel-2 images acquired within May and June 2026. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

Europe,SENTINEL2_MOSAIC_20260501_20260611_RGB,GeoTIFF,WCS

June 1, 2026, 2 a.m. Sept. 30, 2026, 2 p.m.

Sentinel-2 tiny sample collection for testing purposes.
Tiny sample Sentinel-2 data collection (different dimension names for temporal (time) and bands (band) compared to the SENTINEL2_L2A_SAMPLE collection), generated using openEO, stored initially as netCDF and splitted later into separate COGs (one for each band and each date) for more interoperability.

STAC

Sentinel-2,openEO,CDSE

June 2, 2022, 2 a.m. June 30, 2022, 2 a.m.

Sentinel-2 tiny sample collection for testing purposes.
Tiny sample Sentinel-2 data collection, generated using openEO, stored initially as netCDF and splitted later into separate COGs (one for each band and each date) for more interoperability.

STAC

Sentinel-2,openEO,CDSE

June 2, 2022, 2 a.m. June 30, 2022, 2 a.m.

SENWISE_Input_Daytime_land_surface_temperature_Representative_Dataset
Daytime land surface temperature (LST) data at 60m resolution. Data are produced starting from ESA CCI LST product at 1km resolution. Data are provided in Kelvin and stored with an offset of 150 and a scale factor of 0.01.

STAC

LSTM-LIKE,LST,HIGH RESOLUTION,KDD,CCI,SENWISE,SENTINEL

April 1, 2017, 2 a.m. Nov. 1, 2017, 12:59 a.m.

SENWISE Input LSTM Representative Dataset
Representative bands for the Land Surface Temperature Monitoring consolidated within the SENWISE initiative, gathered from sources as Sentinel-2, Sentinel-3 enhanced at high resolution, and simulation alogrithms

STAC

LSTM,LST,HIGH RESOLUTION

July 6, 2020, 12:22 p.m. July 6, 2020, 12:40 p.m.

Snow cover phenology
The snow cover phenology dataset was generated by exploiting the snow product MOD10A1.061 and contains the following variables: first snow day (FSD), last snow day (LSD), yearly averaged snow cover area (SCA), snow cover duration (SCD).

STAC

Collection,Snow cover phenology,FSD,LSD,SCA,SCD,MODIS,MODIS-Terra,Hydrography

Oct. 1, 2000, 2 a.m. Sept. 30, 2023, 2 a.m.

Snow depth - Venosta Valley
Daily snow depth [mm] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

collection,snow,depth,geotop,model,No platform assigned,Land use,Land cover

Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 a.m.

Snow depth - Venosta Valley
Daily snow depth [mm] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

STAC

snow,depth,geotop,model

Sept. 2, 2019, 2 a.m. Aug. 2, 2020, 2 a.m.

Snow Water Equivalent-1000m
A 1km experimental dataset for the Mediterranean terrestrial region of Snow Water Equivalent from the assimilation of Sentinel-1 C-SNOW data into the S3M model

STAC

SWE,SENTINEL-1,SNOW

Aug. 1, 2015, 2 a.m. Dec. 31, 2021, 1 a.m.

SoilGrids data for the European Alps
SoilGrids is a system for global digital soil mapping that uses state-of-the-art machine learning methods to map the spatial distribution of soil properties across the globe. SoilGrids prediction models are fitted using over 230 000 soil profile observations from the WoSIS database and a series of environmental covariates. Covariates were selected from a pool of over 400 environmental layers from Earth observation derived products and other environmental information including climate, land cover and terrain morphology. The outputs of SoilGrids are global soil property maps at six standard depth intervals (according to the GlobalSoilMap IUSS working group and its specifications) at a spatial resolution of 250 meters.

STAC

soil,buik density,sand,silt,soil thickness,clay,PH,organic content

March 1, 2020, 1 a.m. March 1, 2020, 1 a.m.

Soil moisture and temperature datasets in LIDO orchard
Soil moisture and temperature timeserie datasets of the apple orchard located in the Laimburg Integrated Digital Orchard in Bolzano province. The timeseries collects data about soil moisture and temperature at 20 cm, 30 cm and 40 cm under the soil surface in 3 different position of the orchard. Acquisition frequency is 30 minutes for all the parameters.

InfluxDB

soil,temperature,water,soil moisture,cct,Soil,Agricultural and aquaculture facilities,agriculture,climate,water,soil

May 26, 2023, 6 p.m. None

Soil Moisture Anomalies - ERA5_QM
The Soil Moisture Anomaly is a drought indicator used to detect and monitor agricultural drought conditions, defined by a prolonged period of deficit in the availability of soil moisture to plants. The soil moisture anomalies provided as part of the Alpine Drought Observatory was derived from ERA5 Volumetric Soil Water Layers at different depths with Layer 1 (0-7cm), Layer 2 (7-28cm), Layer 3 (28–100cm), and Layer 4 (100-289cm). The input ERA5 soil moisture dataset was downscaled using a quantile mapping approach. Daily anomalies were calculated using a climatological mean and standard deviation of soil moisture from a smoothed time-series (running mean on a 10-day window) for a reference period of 1981–2020. The Soil Moisture Anomalies values range from -5 to +5, with negative values indicating drier than conditions while positive values indicate wetter than normal conditions, and -1 to +1 values indicates near-normal conditions. Datasets contains modified Copernicus Climate Change Service Information [1980–current year]; contains modified Copernicus Atmosphere Monitoring Service Information [1980-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

STAC

SOIL MOISTURE,ALPINE DROUGHT OBSERVATORY,SOIL MOISTURE ANOMALIES,ERA5,ADO PROJECT,ADO

Dec. 31, 1979, 1 p.m. None

Soil Moisture Anomalies - ERA5_QM
The Soil Moisture Anomaly is a drought indicator used to detect and monitor agricultural drought conditions, defined by a prolonged period of deficit in the availability of soil moisture to plants. The soil moisture anomalies provided as part of the Alpine Drought Observatory was derived from ERA5 Volumetric Soil Water Layers at different depths with Layer 1 (0-7cm), Layer 2 (7-28cm), Layer 3 (28–100cm), and Layer 4 (100-289cm). The input ERA5 soil moisture dataset was downscaled using a quantile mapping approach. Daily anomalies were calculated using a climatological mean and standard deviation of soil moisture from a smoothed time-series (running mean on a 10-day window) for a reference period of 1981–2020. The Soil Moisture Anomalies values range from -5 to +5, with negative values indicating drier than conditions while positive values indicate wetter than normal conditions, and -1 to +1 values indicates near-normal conditions. Datasets contains modified Copernicus Climate Change Service Information [1980–current year]; contains modified Copernicus Atmosphere Monitoring Service Information [1980-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

OpenEO

collection,Soil Moisture,Alpine Drought Observatory,Soil Moisture Anomalies,ERA5,Collection,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1979, 1 p.m. May 27, 2024, 2 p.m.

Solar energy in alpine historic buildings
Layer to represent the position of seven case study buildings of exemplary energy efficient interventions in historic buildings. In all buildings, a photovoltaic or solar thermal system was integrated as one of the renovation measures.

Maps

Europe,energy refurbishment,historic buildings

Solar energy in alpine historic buildings tour
Layer to represent the "tour" to seven case study buildings of exemplary energy efficient interventions in historic buildings. In all buildings, a photovoltaic or solar thermal system was integrated as one of the renovation measures.

Maps

Europe,historic buildings,virtual tour

Solar Irradiation - Monthly Mean Annual Average
Monthly Mean Annual Average of Solar Irradiation in kW/h.

Maps

Italy,GeoTIFF,irradiation,solar,WCS

Southtyrol: Administrative Units and Municipalities
This layer shows the municipality boundaries for Southtyrol, the Autonomous Province of Bolzano/Bozen.

Maps

Italy,administrative,boundaries,municipality

SOUTH TYROL: Hexagonal tessellation (~250m)
Tessellation onto regular hexagonal cells of South Tyrol (IT) at a resolution of ~250m.

Maps

Austria,features,st_tessellation_250m_pol,suedtirol,tessellation

SOUTH TYROL: Home->work trips on tessellated roads network
Projection of home->work trips in South Tyrol onto OSM drivable roads network. Roads themselves are projected onto a 250m hexagonal tessellation.

Maps

Italy,features,Population,st_pop_flow_rds_ln_s3_apb_250m,flow,tessellated

SOUTH TYROL: Home->work trips on tessellated roads network (dynamic roads load)
Projection of home->work trips in South Tyrol onto OSM drivable roads network. Roads themselves are projected onto a 250m hexagonal tessellation. Edge load is updated at every new trip projection.

Maps

Italy,features,st_pop_flow_rds_ln_s3_apb_250m_dyn,flow

SOUTH TYROL: impacts of flood events (ED30)
Impacts of flood events in South Tyrol (IT) taken from the ED30 database.

Maps

Italy,features,st_hzd_evnt_hydro_ed30_apb_pnt_all,ed30

SOUTH TYROL: impacts of landslide events (IFFI)
Impacts of landslides events in South Tyrol (IT) taken from the IFFI (Inventory of Landslide Phenomena in Italy) database [last update 18 Nov 2022].

Maps

Italy,events,features,IFFI,landslides,south tyrol,st_hzd_evnt_landslides_pt_s4_p_iffi

South Tyrol Land Use Land Cover (Level 1)
This layer shows the level 1 land use land cover classes for the Province of South Tyrol.

Maps

Italy,land cover,landuse,South Tyrol

SOUTH TYROL: Population Density (Ago 2015, 02PM, 100m)
Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 PM (Aug 2015 run).

Maps

Italy,August,GeoTIFF,Population,ST_pop_100m_Aug2015_1400_WD_AllAgeGroups_ras,WCS

SOUTH TYROL: Population Density (Aug 2015, 02AM, 100m)
Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 AM (Aug 2015 run).

Maps

Italy,August,GeoTIFF,Population,ST_pop_100m_Aug2015_0200_WD_AllAgeGroups_ras,WCS

SOUTH TYROL: Population Density (Feb 2015, 02PM, 100m)
Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 PM (Feb 2015 run).

Maps

Italy,February,GeoTIFF,Population,ST_pop_100m_Feb2015_1400_WD_AllAgeGroups_ras,WCS

SOUTH TYROL: Population flow comparison with traffic counts
Absolute difference between traffic counts in the 5-9 AM time interval (2021 averages) and the commuting population flow model output.

Maps

Italy,commuting,features,flow,st_traffic_vs_flow_250tess_2021,traffic,validation

Southtyrol Settlements
No abstract provided

Maps

Italy,settlements,Southtyrol

SOUTH TYROL: tessellated OSM drivable roads (~250m)
Drivable roads from OpenStreetMap over South Tyrol (IT) onto an hexagonal tessellation of ~250m.

Maps

Italy,drive,features,south tyrol,st_tran_rds_ln_s4_osm_pp_drive_250tess,tessellation

SOUTH TYROL: tessellated OSM drivable roads with traffic simulation (~250m)
Drivable roads from OpenStreetMap over South Tyrol (IT) onto an hexagonal tessellation of ~250m, where the weight of each edge is reduced by an amount thatis proportional to the simulation of home->work traffic of South Tyrol.

Maps

Italy,features,st_tran_rds_ln_s4_osm_pp_drive_250tess_dyn

SOUTH TYROL: Tessellated population day/night (~250m)
Multi-temporal aggregated population data over South Tyrol onto an hexagonal tessellation of ~250m. Day-time, night-time and commuting time are available.

Maps

Italy,day,features,night,Population,st_pop_pol_s3_250m_daynight

SOUTH TYROL: Traffic counts per hour [2021]
2021 yearly averages of traffic counts per each hour of the day, over South Tyrol.

Maps

Italy,features,hour,st_trans_traffic_counts_hourly_average_2021_apb_pnt,traffic

SOUTH TYROL: Traffic Report (current situation)
Accumulated records of the traffic situation over the roads of South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

Italy,features,southtyrol_tran_traffic_report_pt_s4_pa_pp,traffic

SOUTH TYROL: Traffic Report (mountain roads and passes)
Accumulated records of traffic events over mountain roads and passes in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

Italy,features,mountain,southtyrol_tran_traffic_report_pt_s4_pa_pp_mroads_and_passes,traffic

SOUTH TYROL: Traffic Report (neighbouring countries)
Accumulated records of traffic events related to border areas in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

Italy,border,features,southtyrol_tran_traffic_report_pt_s4_pa_pp_neigh_countries,traffic

SOUTH TYROL: Traffic Report (public transports)
Accumulated records of traffic events related to public transports in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

Italy,public,southtyrol_tran_traffic_report_pt_s4_pa_pp_public_transports,traffic

SOUTH TYROL: Traffic Report (road works and locks)
Accumulated records of works and locks over the roads of South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

Italy,features,locks,southtyrol_tran_traffic_report_pt_s4_pa_pp_works_and_locks,traffic,works

SOUTH TYROL / VAIA: Landslide probability maps
Polygonized time-series of landslide susceptibility (%) over South Tyrol.

Maps

Italy,susceptibility,landslides,st_hzd_pred_landslides_pol_s4_p_polygonized

SSEBop Evapotranspiration - 1 km
Operational FEWS NET ET product over the Alps

STAC

evapotranspiration,ssebop,energy balance,MOD16i,cct

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

SSEBop Evapotranspiration - 1 km
Operational FEWS NET ET product over the Alps

Other

collection,evapotranspiration,ssebop,energy balance,MOD16i,cct,Aqua, Terra,Land use,Land cover

Jan. 11, 2003, 1 a.m. Feb. 1, 2020, 1 a.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPEI,STANDARDISED PRECIPITATION-EVAPOTRANSPIRATION INDEX,SURFACE WATER BALANCE ANOMALIES,ERA5,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. May 27, 2024, 2 p.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-12
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPEI,STANDARDISED PRECIPITATION-EVAPOTRANSPIRATION INDEX,SURFACE WATER BALANCE ANOMALIES,ERA5,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-12
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-2
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-2
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPEI,STANDARDISED PRECIPITATION-EVAPOTRANSPIRATION INDEX,SURFACE WATER BALANCE ANOMALIES,ERA5,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-3
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-3
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPEI,STANDARDISED PRECIPITATION-EVAPOTRANSPIRATION INDEX,SURFACE WATER BALANCE ANOMALIES,ERA5,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPEI,STANDARDISED PRECIPITATION-EVAPOTRANSPIRATION INDEX,SURFACE WATER BALANCE ANOMALIES,ERA5,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-1
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Standardised Precipitation Index - ERA5_QM SPI-1
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPI,STANDARDISED PRECIPITATION INDEX,PRECIPITATION ANOMALIES,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-12
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPI,STANDARDISED PRECIPITATION INDEX,PRECIPITATION ANOMALIES,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-12
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Standardised Precipitation Index - ERA5_QM SPI-2
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPI,STANDARDISED PRECIPITATION INDEX,PRECIPITATION ANOMALIES,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-2
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Standardised Precipitation Index - ERA5_QM SPI-3
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Oct. 2, 2023, 2 p.m.

Standardised Precipitation Index - ERA5_QM SPI-3
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPI,STANDARDISED PRECIPITATION INDEX,PRECIPITATION ANOMALIES,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-6
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SPI,STANDARDISED PRECIPITATION INDEX,PRECIPITATION ANOMALIES,ADO PROJECT,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Precipitation Index - ERA5_QM SPI-6
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. April 24, 2019, 2 p.m.

Standardised Snow Pack Index - ERA5_QM SSPI-10
The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SSPI,SSPI_10D,STANDARDISED SNOW PACK INDEX,ERA5,SNOWGRID,ADO

Dec. 31, 1978, 1 p.m. None

Standardised Snow Pack Index - ERA5_QM SSPI-10
The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SSPI,standardised snow pack index,ERA5,SNOWGRID,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Nov. 27, 2023, 1 p.m.

Standardised Snow Pack Index - ERA5_QM SSPI-30
The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

OpenEO

collection,SSPI,standardised snow pack index,ERA5,SNOWGRID,cct,N/A,Land use,Land cover

Dec. 31, 1978, 1 p.m. Nov. 27, 2023, 1 p.m.

Standardised Snow Pack Index - ERA5_QM SSPI-30
The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

STAC

SSPI,SSPI_30D,STANDARDISED SNOW PACK INDEX,ERA5,SNOWGRID,ADO

Dec. 31, 1978, 1 p.m. None

ST_GRIDDED_TIME_SERIES_PRECIPITATION
The product contains the gridded daily series of mean precipitation at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

STAC

Precipitation,Daily,High-resolution,cct

Jan. 1, 2020, 1 p.m. Jan. 1, 2023, 1 p.m.

ST_GRIDDED_TIME_SERIES_PRECIPITATION
The product contains the gridded daily series of mean precipitation at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

OpenEO

collection,Precipitation,Daily,High-resolution,cct,No platform assigned,Land use,Land cover

Jan. 1, 2020, 1 p.m. Jan. 1, 2023, 1 p.m.

ST_GRIDDED_TIME_SERIES_TEMPERATURE
The product contains the gridded daily series of mean temperature at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset currently spans the period 1980 – 2020, but it is expected to be regularly updated. It was obtained by applying an anomaly-based interpolation to the observations of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All station series used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the daily average of maximum and minimum temperature. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

OpenEO

collection,Temperature,Daily,High-resolution,cct,No platform assigned,Land use,Land cover

Jan. 1, 2020, 1 p.m. Jan. 1, 2023, 1 p.m.

ST_GRIDDED_TIME_SERIES_TEMPERATURE
The product contains the gridded daily series of mean temperature at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset currently spans the period 1980 – 2020, but it is expected to be regularly updated. It was obtained by applying an anomaly-based interpolation to the observations of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All station series used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the daily average of maximum and minimum temperature. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

STAC

Temperature,Daily,High-resolution,cct

Jan. 1, 2020, 1 p.m. Jan. 1, 2023, 1 p.m.

ST_MONTHLY_GRIDDED_CLIMATOLOGIES_PRECIPITATION
The product contains the gridded climatologies of monthly total precipitation for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database:https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

OpenEO

collection,Temperature,Climatologies,Daily,High-resolution,cct,No platform assigned,Land use,Land cover

Jan. 1, 1980, 1 a.m. Dec. 31, 2010, 1 a.m.

ST_MONTHLY_GRIDDED_CLIMATOLOGIES_PRECIPITATION
The product contains the gridded climatologies of monthly total precipitation for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database:https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

STAC

Temperature,Climatologies,Daily,High-resolution,cct

Jan. 1, 1981, 1 p.m. Jan. 1, 2010, 1 p.m.

ST_MONTHLY_GRIDDED_CLIMATOLOGIES_TEMPERATURE
The product contains the gridded climatologies of monthly mean temperature for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database:https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the average of maximum and minimum temperature. The climatologies represent the mean values over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021).

OpenEO

collection,Temperature,Climatologies,Daily,High-resolution,cct,No platform assigned,Land use,Land cover

Jan. 1, 1980, 1 a.m. Dec. 31, 2010, 1 a.m.

ST_MONTHLY_GRIDDED_CLIMATOLOGIES_TEMPERATURE
The product contains the gridded climatologies of monthly mean temperature for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database:https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the average of maximum and minimum temperature. The climatologies represent the mean values over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021).

STAC

Temperature,Climatologies,Daily,High-resolution,cct

Jan. 1, 1981, 1 p.m. Jan. 1, 2010, 1 p.m.

Süd-Ago-Ost: Betweenness centrality on OSM drivable roads
Betweenness centrality topologic indicator calculated on the OSM drivable roads over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

Austria,betweenness centrality,drive,features,osm,sudagoost_tran_rds_ln_s4_osm_pp_drive_betwcentr

Süd-Ago-Ost: Hospitals accessibility on OSM drivable roads
Hospital accessibility indicator calculated on the OSM drivable roads over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

Austria,accessibilty,features,osm,sudagoost_tran_rds_ln_s4_osm_pp_drive_hospacc

Sept. 28, 2021, 2 p.m. Sept. 29, 2021, 2 p.m.

Süd-Ago-Ost: OSM drivable roads
Drivable roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

Austria,drive,features,osm,sudagoost_tran_rds_ln_s4_osm_pp_drive

Süd-Ago-Ost: OSM main roads
Main roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

Austria,features,main,osm,sudagoost_tran_rds_ln_s3_osm_pp_main

Suitable areas in Canton Ticino for e-bike chargers
Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in Canton Ticino (Switzerland).

Maps

Switzerland,e-mobility

Suitable areas in Canton Ticino for e-car chargers
Layer to represent the most suitable locations for installing charging infrastructure for e-cars in Canton Ticino (Switzerland).

Maps

Switzerland,e-mobility

Suitable areas in South Tyrol for e-bike chargers
Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in South Tyrol.

Maps

Italy,e-mobility

Suitable areas in South Tyrol for e-car chargers
Layer to represent the most suitable locations for installing charging infrastructure for e-cars in South Tyrol.

Maps

Italy,e-mobility

Suitable areas in Verbano-Cusio-Ossola for e-bike chargers
Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in Verbano-Cusio-Ossola province.

Maps

Italy,e-mobility

Suitable areas in Verbano-Cusio-Ossola for e-car chargers
Layer to represent the most suitable locations for installing charging infrastructure for e-cars in Verbano-Cusio-Ossola province.

Maps

Italy,e-mobility

Synthetic Building Energy Consumption Dataset – Local Calendar Week Sampler (MODERATE Project)
This dataset contains 50 synthetic years of sub-hourly building energy and indoor environment data, generated from approximately 3 years of real monitored data using the Local Calendar Week Sampler method developed within the MODERATE project (Horizon Europe GA 101069834). The synthetic generation approach recombines true historical weekly profiles into statistically plausible synthetic ones, preserving seasonal patterns, weekday/weekend variation, and distributional characteristics of the original data, while providing anonymization through resampling, donor diversification, and a percentile-based source-recognizability guardrail. Each synthetic year covers a full calendar year at 2-hour temporal resolution, with the following variables: Column Unit Description timestamp — Date and time (2h intervals) el. Energy kWh Electrical energy consumption th. Energy kWh Thermal energy consumption CO2 ppm Indoor CO₂ concentration Temperature °C Indoor air temperature WZ warm_water_energy kWh Domestic hot water energy ext. Solar Irradiance W/m² External solar irradiance ext. Temperature °C External air temperature synthetic_year_id — Identifier of the synthetic year (001–050) Generation Method The Local Calendar Week Sampler operates as follows: 1. Historical weeks are validated and flagged for gaps (>3h) or extreme values (>95th percentile). 2. For each target week, a candidate pool of temporally aligned historical weeks is assembled. 3. A primary donor (donor A) provides context variables (CO₂, temperature); a secondary daily donor (donor B) provides energy shape variation. 4. The synthetic profile is constructed as a scaled convex combination of donor profiles, anchored to a candidate-pool median baseline. 5. Weekly totals are rescaled to match a randomly selected week from the candidate pool. 6. A cosine transition layer is applied at week boundaries for temperature and CO₂ to avoid discontinuities. 7. Data is resampled to 2-hour resolution (energy summed, other variables averaged). Anonymization measures include donor energy contribution capping (10–35%), CO₂ anomaly screening, resampling, and a source-recognizability guardrail that resamples weeks where the original donor remains the closest shape match. Quality Metrics Statistical fidelity of the synthetic data relative to historical data was assessed using shape-based metrics across all 50 generated years: Metric Value Mean nearest weekly NRMSE 2.008 Median nearest weekly NRMSE 1.605 Weekly close-match rate (p01) 0.177 Weekly close-match rate (p05) 0.329 Source-is-nearest-weekly rate 0.095 Mean source shape correlation 0.487 Source-is-top-shape-match rate 0.201 Dominant candidate share 0.025 Normalized candidate usage entropy 0.982 The high candidate usage entropy (0.982) indicates well-distributed sampling with no dominant source weeks. The source-is-nearest rate of 9.5% is low, indicating effective anonymization. Approximately 40% of synthetic days have a historical near-duplicate below the empirical non-self NRMSE threshold — a known limitation without formal anonymization guarantees. Seasonal daily profiles and distributions of electrical energy, thermal energy, and domestic hot water energy were validated visually against historical data: Limitations No formal anonymization guarantees are provided. Thermal energy exhibits near-zero values in summer (near-heating-off conditions), which may amplify relative NRMSE in that season. The dataset does not include the original historical data. Related Resources MODERATE project: moderate-project.eu Grant Agreement No.: 101069834 MODERATE open-source tools: github.com/MODERATE-Project MODERATE platform: moderate.cloud

Other

None

Temperature Condition Index - 231 m 8 days
The Temperature Condition Index (TCI) is based on the Land Surface Temperature (LST) MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The TCI is calculated using the formula TCIi = (LSTmax,i - LSTi)/(LSTmax,i - LSTmin,i) * 100. The TCI expresses anomalies of the LST. The data is provided as 8 day measures. The time series is starting from 2001. The TCI values range from 0-100, whereas high values correspond to optimal vegetation conditions and low values indicate unfavorable vegetation conditions.

STAC

TEMPERATURE CONDITION INDEX,TCI,MODIS,ADO PROJECT,ADO

Jan. 1, 2001, 1 a.m. None

Test layer alternative fuel pag.10
iMONITRAF layer, Alternative fuel pag 10 of the document

Maps

Europe,altern_fuel_10,features

The Alpine Drought Impact report Inventory (EDIIALPS)
The presented data of the Alpine Drought Impact report Inventory (EDIIALPS V1.1) is a slight update of the version 1.0 with some new entries until 2020. The EDIIALPS archives negative drought impacts as text-reports in the "Alpine Space" region and is a sub database of the European Drought Impact report Inventory (EDII). The data resulted from collaborative work from various project partners.

Other

drought,alps,impact,database,text data,cct

June 1, 1503, 1 a.m. Oct. 1, 2021, 2 p.m.

The Geoffrey's Tube Z3950 Server (Sample Record - Please Delete!)
This catalog is for registering all metadata records held by the Geofffrey's Tube Palace Hotel Ballroom.

Other

None

Tofana Global Position System receiving station
Global Position System receiving station timeseries datasets. The receiving station is located on the above infrastructure for the cable lift of Freccia nel Cielo in Tofana di Mezzo at about 2800 m above the sea level. The main goal is monitoring the movement of the cable infrastructure, in the contest of the RESCUE PERMAFROST project .Time interval is double: 1 hour and 1 day. Spatial Reference System is EPSG:32633.

InfluxDB

GPS,permafrost,GNSS,sensor,Buildings,Geology,Transport networks,Elevation,natural dynamics,natural areas, landscape, ecosystems,climate

Sept. 25, 2023, 2 a.m. Oct. 10, 2023, 2 a.m.

TRANSALP Study Area
Extent of the cross-border study area for the TRANSALP project, which includes South Tyrol (IT), Valle Agordino (Veneto, IT), and East Tyrol (AT).

Maps

Austria,extent,features,study,transalp_test_site_extent_pol_pp

TRANSALP Study Area Agordino - Valle del Cordevole
This layer shows the geographic extent of the TRANSALP study area Agordino - Valle del Cordevole.

Maps

Italy,extent,Veneto

TRANSALP Study Area Agordino - Valle del Cordevole (IT)
This layer shows the spatial extent of the Transalp study area Agordino - Valle del Cordevole.

Maps

Global,extent,study area,Veneto

TRANSALP Study Area: Betweenness centrality on tessellated OSM drivable roads
Betweenness centrality topologic indicator calculated on the OSM drivable roads over the trans-national area of South Tyrol (IT), Agordino (Veneto, IT) and East Tyrol (AU). Roads have been projected onto a 250m regular hexagonal tessellation before analysis.

Maps

Austria,features,drive,betweenness centrality,osm,tessellation,transalp_tran_rds_ln_s4_osm_pp_drive_250tess_betwcentr

TRANSALP Study Area: CORINE Land Cover 2018
The Copernicus "CORINE Land Cover" dataset of 2018, clipped over the TRANSALP project cross-border test area.

Maps

Austria,features,transalp_landuse_corine_pol_pp_2018

TRANSALP Study Area East Tyrol
This layer shows the spatial extent of the TRANSALP study area East Tyrol.

Maps

Austria,East tyrol,study area

TRANSALP Study Area: Hexagonal tessellation (~250m)
Tessellation onto regular hexagonal cells of the TransAlp project's study area, which comprises South Tyrol (IT), Valle Agordina (Veneto) and East Tyrol (AU), at a resolution of ~250m.

Maps

Austria,features,tessellation,transalp,transalp_tesselation_250m_cross_border_studyarea

TRANSALP Study Area: Hospitals accessibility on tessellated OSM drivable roads
Hospital accessibility indicator calculated on the tessellated OSM drivable roads over the trans-national area covering South Tyrol (IT), Agordino (Veneto, IT), and East Tyrol (AU).

Maps

Austria,features,hospitals,accessibilty,tessellation

TRANSALP Study Area South Tyrol
This layer shows the geographic extent of the TRANSALP study area South Tyrol.

Maps

Italy,extent,South Tyrol

TRANSALP Study Area: Tessellated exposed assets (~250m)
Aggregated data from exposed assets over the TRANSALP project cross-border study area onto an hexagonal tessellation of ~250m.

Maps

Austria,exposure,features,tessellation,transalp_exposure_assets_pol_s3_250m

TRANSALP Study Area: tessellated OSM drivable roads (~250m)
Drivable roads from OpenStreetMap over TransAlp project's study area (South Tyrol, East Tyrol, Valle Agordina) onto an hexagonal tessellation of ~250m.

Maps

Austria,drive,features,tessellation,transalp,transalp_tran_rds_ln_s4_osm_pp_drive_250tess

TRANSALP Study Area: Tessellated population (~250m)
Aggregated population data over the TransAlp project's study area, which comprises South Tyrol (IT), Valle Agordina (Veneto) and East Tyrol (AU), onto an hexagonal tessellation of ~250m.

Maps

Austria,features,Population,tessellation,transalp_study_area_tesselation_population_pol

Tree_Mask_2018
This layer represents the first draft of a forest mask for the province of South Tyrol based on classification of Sentinel-2 images acquired between 2015 and 2018.

Maps

South Tyrol,Forest,forest mask,south tyrol

Tree_Mask_2018_final
Final Tree Mask of 2018 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2015-2018 as well as slope and elevation information from the EU-DEM.

Maps

South Tyrol,Forest,forest map,south tyrol

Tree_Mask_2019
Tree Mask of 2019 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2017-2019 as well as slope and elevation information from the EU-DEM. Changes since mapping began in 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data).

Maps

South Tyrol,Forest,forest mask,south tyrol

Tree_Mask_2020
Tree Mask of 2020 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2018-2020 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data).

Maps

South Tyrol,Forest,forest mask,south tyrol

Tree_Mask_2021
Tree Mask of 2021 for the province of South Tyrol based on the classification of spectral-temporal metrics of Sentinel-2 images between 2019-2021 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data).

Maps

Global,Forest,forest map,south tyrol

Tree_Mask_2022
Tree Mask of 2022 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2020-2022 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data).

Maps

South Tyrol,Forest,forest map,south tyrol

Trentino - Alto Adige Municipality labels
This Layer displays municipalities labels in Trento and Bolzano provinces.

Maps

Global,comuni_TN_BZ,features,label,municipality

TVO: OSM drivable roads
OpenStreetMap drivable roads over Trentino Alto-Adige, Veneto and Osttirol (Austria).

Maps

Austria,drive,osm,tvo_tran_rds_ln_s3_osm_pp_drive

Tyrol: Roads Network
Lineares Referenzsystem der Verkehrsinfrastrukturen von Tirol - beinhaltet Hochrangiges Strassen- und Bahnnetz bis hin zu den Fuss- und Wanderwegen. Originaldatensatz wird in der Graphenintegrations-Plattform Tirol gewartet.

Maps

Austria,features,pa,roads,tirol_trans_roads_paths_ln_s5_pp

UAS-AED Coverage of South Tyrol
Coverage of the proposed UAS-AED Network of South Tyrol.

Maps

South Tyrol,cartography,defibrillator,drone

April 1, 2023, 2 a.m. Sept. 30, 2023, 2 a.m.

UAS-AED Density of South Tyrol
Density of the proposed UAS-AED Network of South Tyrol.

Maps

South Tyrol,cartography,defibrillator,drone

April 1, 2023, 2 a.m. Sept. 30, 2023, 2 a.m.

UAS-AED Influence Areas of South Tyrol
UAS-AED Stations Influence Areas (Thiessen Polygons) of South Tyrol.

Maps

South Tyrol,cartography,defibrillator,drone

April 1, 2023, 2 a.m. Sept. 30, 2023, 2 a.m.

UAS-AED Suitability of South Tyrol
UAS-AED Suitability for the proposed UAS-AED Network of South Tyrol.

Maps

South Tyrol,cartography,defibrillator,drone

April 1, 2023, 2 a.m. Sept. 30, 2023, 2 a.m.

UAS Feasibility Land Use of South Tyrol
UAS Feasibility Land Use of South Tyrol.

Maps

South Tyrol,cartography,defibrillator,drone

April 1, 2023, 2 a.m. Sept. 30, 2023, 2 a.m.

UAV DataCubes
High resolution - hyperspectral maps as part of the data time series produced by MONALISA project. 16 Hyperspectral bands and Orthomosaic and Digital Surface models

OpenEO

collection,UAV,Multispectral,Suface models,UAV octocopter,Land use,Land cover

Sept. 4, 2019, 2 a.m. Sept. 4, 2019, 2 a.m.

UAV DataCubes 20150507
HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

collection,UAV,Multispectral,Suface models,Soleon,Land use,Land cover

May 7, 2015, 2 a.m. May 7, 2015, 2 a.m.

UAV DataCubes 20150821
HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

collection,UAV,Multispectral,Suface models,Soleon,Land use,Land cover

Aug. 21, 2015, 2 a.m. Aug. 21, 2015, 2 a.m.

UAV DataCubes 20150909
HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

collection,UAV,Multispectral,Suface models,Soleon,Land use,Land cover

Sept. 9, 2015, 2 a.m. Sept. 9, 2015, 2 a.m.

Vegetation Condition Index - 231 m 8 days
The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

OpenEO

collection,vegetation condition index,vci,modis,ADO project,ADO,Terra,Land use,Land cover

Jan. 1, 2001, 1 a.m. Aug. 29, 2022, 2 a.m.

Vegetation Condition Index - 231 m 8 days
The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

STAC

VEGETATION CONDITION INDEX,VCI,MODIS,ADO PROJECT,ADO

Jan. 1, 2001, 1 a.m. None

Vegetation Health Index - 231 m 8 days
The Vegetation Health Index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and the land surface temperature, both derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance and the land surface temperature (LST) on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m, therefore the original 1000 m resolution of the MOD11A2 LST is downscaled to 231 m of the MOD09Q1 reflectance. Both products are masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as 8 day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

OpenEO

collection,vegetation health index,vhi,modisi,ADO project,ADO,Terra,Land use,Land cover

Jan. 1, 2001, 1 a.m. Aug. 29, 2022, 2 a.m.

Vegetation Health Index - 231 m 8 days
The Vegetation Health Index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and the land surface temperature, both derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance and the land surface temperature (LST) on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m, therefore the original 1000 m resolution of the MOD11A2 LST is downscaled to 231 m of the MOD09Q1 reflectance. Both products are masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as 8 day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.

STAC

VEGETATION,HEALTH INDEX,VHI,MODIS,ADO PROJECT,ADO

Jan. 1, 2001, 1 a.m. None

Veneto: Roads Network
Rete stradale derivata da DataBase strati prioritario in scala 1:10.000 (Regione Veneto,Sezione Pianificazione Territoriale Strategica e Cartografia)

Maps

Italy,features,pa,roads,veneto_tran_rds_ln_s4_pa_pp

Vulnerability indicator
Vulnerability indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project

Maps

Alpine region,Alps,Eurac,snow tourism destinations,vulnerability

Warm Spell Duration Index in Trentino-South Tyrol, IT (1971-2100)
Annual count of days with at least 6 consecutive days when TX > 90th percentile

OpenEO

collection,wsdi,FAct CLIMAX,Trentino,South Tyrol,Projection,No platform assigned,Land use,Land cover

Jan. 1, 1970, 1 a.m. Jan. 1, 2102, 12:59 a.m.

Water Heating
The Layer of share of final energy consumption in the residential sector for water heating. The frequency of data is annual.

Maps

Europe,cct,consumption,energy,heating,water

Jan. 1, 2018, 12:41 p.m. Dec. 31, 2018, 12:41 p.m.

Watt's Up? Synthetic Data for Buildings
This repository contains the challenge data and links to the participating teams' repositories for the Watt's Up? Hack for Energy Efficiency – Synthetic Data for Buildings hackathon, held at TU Wien on 22–23 February 2025. 🔗 Hackathon event page This hackathon was funded by the EU project MODERATE (grant No 101069834) and VSC/EuroCC.

Other

None

Weekly-aggregated irrigation product - Ebro basin
A 1 km experimental dataset of weekly-aggregated irrigation estimates retrieved through the SM-based inversion approach implemented with RT1 Sentinel-1 soil moisture and ERA5-Land rainfall and GLEAM 1 km potential evaporation

STAC

IRRIGATION,IRRIGATION WATER USE,SENTINEL-1

Jan. 1, 2016, 1 a.m. Dec. 31, 2021, 1 a.m.

Weekly-aggregated irrigation product - Po basin
A 1 km experimental dataset of weekly-aggregated irrigation estimates retrieved through the SM-based inversion approach implemented with RT1 Sentinel-1 soil moisture and ERA5-Land rainfall and GLEAM 1 km potential evaporation

STAC

IRRIGATION,IRRIGATION WATER USE,SENTINEL-1

Jan. 1, 2016, 1 a.m. Dec. 31, 2021, 1 a.m.

Wetlands_Dunes_Circeo_national_park
This collection is based on Sentinel-2 level 2A dataset acquired from both Sentinel-2 A and B satellites. It covers a temporal range of 01-02-2021 to 30-09-2023, for every site all Sentinel scenes covering the area are included. The collection includes all bands at a resolution of 10 meters. The bands are listed below. B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12 SCL The Level-2A data includes a Scene Classification, the algorithm allows the detection of clouds, snow and cloud shadows and generation of a classification map. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters during the atmospheric correction step. The L2A SCL map can also be a valuable input for further processing steps or data analysis. The collection covers all sites of the Hyperecos project. The following sites are included with the spatial extent mentioned. Cireco {'west': 12.870775000000037, 'east': 13.103782000000024, 'south': 41.22186599400004, 'north': 41.41504699400008} Orbetello {'west': 11.181888751927033,'east': 11.455397318265538, 'south': 42.372996996314896, 'north': 42.50311599528868} Sciliar - Catinaccio {'west': 11.506713000000047, 'east': 11.667129000000045, 'south': 46.440615996000076, 'north': 46.54040999600005} Castel Porziano {'west': 12.345466114000033, 'east': 12.451677663000055, 'south': 41.65575504100008, 'north': 41.78393419900004} Monte Bondone {'west': 11.027602448000039, 'east': 11.044658587000072, 'south': 45.98801759300005, 'north': 46.01382867600006} Bosco Fontana {'west': 10.73031770800003, 'east': 10.757708439000055, 'south': 45.192834764000054, 'north': 45.20899297600005} Hunsrück-Hochwald {"west": 6.98720747225448, "east": 7.28904390300005, "north": 49.7943764377934, "south": 49.60530142600001}

STAC

HYPERECOS,ASI,SENTINEL-2,SENTINEL2,S2,L2A

May 5, 2021, 2 a.m. Sept. 27, 2023, 2 a.m.

World Land Cover Himalayas 2015
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Asia,ecosystem,landcover

World Land Cover Himalayas 2016
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Himalayas 2017
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Himalayas 2018
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Himalayas 2019
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Himalayas 2020
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Province 2015
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Europe,ecosystem,landcover

World Land Cover Province 2016
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Province 2017
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Province 2018
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Province 2019
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Land Cover Province 2020
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Himalayas 2015
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Asia,ecosystem,landcover

World Terrestrial Ecosystems Himalayas 2016
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Himalayas 2017
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Himalayas 2018
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Himalayas 2019
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Himalayas 2020
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Province 2015
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Europe,ecosystem,landcover

World Terrestrial Ecosystems Province 2016
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Province 2017
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Province 2018
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Province 2019
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

World Terrestrial Ecosystems Province 2020
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

Global,ecosystem,landcover

Yearly damages LATEST
Mapping of the forest changes occurring in 2020, 2021, 2022, 2023 and 2024 at a yearly scale. The outputs shown here are based on the analysis of Sentinel 2 time series. The date corresponds to the first date at which a change was detected.

Maps

South Tyrol,Forest