Corine Land Cover (CLC) 2018
FAIR Overall Score
Abstract
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.
Keywords
Supplemental information
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Legal constraints
CC-BY-4.0
Contact for metadata
Eurac Research - Institute for Earth Observation
bartolomeo.ventura@eurac.edu
Viale Druso, 1 / Drususallee 1, Eurac Research, Bolzano, Autonomous Province of Bolzano, 39100, Italy
FAIR Overall Score: 58%
| Principle | Score | Earned | Level |
|---|---|---|---|
| Findable | 43% | 3 of 7 | initial |
| Accessible | 57% | 4 of 7 | advanced |
| Interoperable | 67% | 4 of 6 | moderate |
| Reusable | 67% | 4 of 6 | moderate |
Evaluated by F-UJI web service: Anusuriya Devaraju, & Robert Huber. (2020). F-UJI - An Automated FAIR Data Assessment Tool. Zenodo. https://doi.org/10.5281/zenodo.6361400
Snippet code
install.packages("openeo")
library(openeo)
# login ----
host = "https://openeo.eurac.edu"
con = connect(host = host)
login()
# check login ---
con$isConnected()
con$isLoggedIn()
describe_account()
# load collection - save result ----
p = processes()
data = p$load_collection(id = "ADO_CORINE_100m_3035",
spatial_extent = list(west = 4.00771,
east = 17.506871,
south = 42.885683,
north = 50.31948),
temporal_extent = list("2018-01-01", "2018-12-31"))
result = p$save_result(data = data, format="netCDF")
# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
format = "netCDF",
output_file = "ADO_CORINE_100m_3035.nc",
con = con)
# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
title = "ADO_CORINE_100m_3035",
description = "ADO_CORINE_100m_3035",
format = "netCDF")
start_job(job = job_id)
result_list = list_results(job = job_id)
download_results(job = job_id, folder = ".")#pip install openeo
import openeo
# login ----
euracHost = "https://openeo.eurac.edu"
conn = openeo.connect(euracHost).authenticate_oidc(client_id="openEO_PKCE")
# load collection - save result ----
data = conn.load_collection("ADO_CORINE_100m_3035",spatial_extent={'west':4.00771,'east':17.506871,'south':42.885683,'north':50.31948},temporal_extent=["2018-01-01", "2018-12-31"])
result = data.save_result(format="NetCDF")
# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_CORINE_100m_3035.nc",format="netCDF")
# or start a batch job (suitable for larger requests, e.g. when .download() timeouts)
job = result.create_job(title = "ADO_CORINE_100m_3035",description = "ADO_CORINE_100m_3035",out_format = "netCDF")
jobId = job.job_id
job.start_job()
jobResults = job.get_results()
jobResults.download_files('.')Related docs
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