BURUNDI: Landslides susceptibility map (national scale)
FAIR Overall Score
Abstract
National-scale unclassified landslide susceptibility map for Burundi.
Keywords
Supplemental information
The map is based on a statistical learning algorithm (Generalized Additive Model) that was trained on the basis of different topographic predictors (slope, topographic position, convergence index, aspect, topographic wetness index, geomorphons), landcover and available landslide inventory data. The model was tested using model-independent landslide data and achieved a high statistical performance (median AUROC 0.89).
Legal constraints
Not Specified: The original author did not specify a license.
Contact for resource
Eurac Research - Center for Climate Change and Transformation
piero.campalani@eurac.edu
Bolzano, ITA
Contact for metadata
Eurac Research - Center for Climate Change and Transformation
stefan.steger@eurac.edu
Download online resources
image/png · BURUNDI: Landslides susceptibility map (national scale) application pdf · BURUNDI: Landslides susceptibility map (national scale) image jpeg · BURUNDI: Landslides susceptibility map (national scale) image png · BURUNDI: Landslides susceptibility map (national scale) image/png8 · BURUNDI: Landslides susceptibility map (national scale) application x-gzip · BURUNDI: Landslides susceptibility map (national scale) image tiff · BURUNDI: Landslides susceptibility map (national scale)
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
Related docs
| # | Name | Description | Link | Date published | Category |
|---|---|---|---|---|---|
| 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 |