RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TQS
FAIR Overall Score
Abstract
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.
Keywords
Supplemental information
https://ai4ebv.eurac.edu/
Legal constraints
Public Domain (PD): Works in the public domain may be used freely without the permission of the former copyright owner. (http://www.copyright.gov/help/faq/faq-definitions.html)
Contact for resource
Eurac Research - Institute for Earth Observation
michele.claus@eurac.edu
Contact for metadata
Eurac Research - Institute for Earth Observation
daniel.frisinghelli@eurac.edu
Download online resources
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FAIR Overall Score: 77%
| Principle | Score | Earned | Level |
|---|---|---|---|
| Findable | 57% | 4 of 7 | moderate |
| Accessible | 100% | 7 of 7 | advanced |
| Interoperable | 67% | 4 of 6 | moderate |
| Reusable | 83% | 5 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
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| 1 | Maps web services URLs | Here you can find all the available web service URLs provided by Maps, to consume datasets through an interoperable client (QGIS, Python, etc.) or to develop codes for analysis. The most relevant services to access datasets are: WMS (Web Map Service) to visualize layers in a map and to query them); WFS (Web Feature Service) or WCS (Web Coverage Service) to download raw datsets and use them for deep analysis or transformation. | Link | Dec. 13, 2021 | Maps |
| 2 | maps.eurac.edu documentation | maps.eurac.edu is based on Geonode, here the link to the official documentation | Link | April 28, 2021 | Maps |