Giant landslide inventory of Patagonia classified with the convolutional neural network AlexNet

DOI

We used the convolutional neural network AlexNet to detect giant landslides (>10^8 m³) along basaltic plateaus in the Patagonian extra-Andean region east of the Andean Cordillera (40°S-53°S, 66°W-72°W). The network was trained using topographic information (elevation, roughness, curvature) from TanDEM-X data. The dataset includes the original raster dataset as well as a polygon dataset. Since the network was trained with terrestrial data, large water bodies, the ocean as well as human settlements are sometimes detected as landslides. We removed the falsely predicted landslides patches in the polygon file of the dataset. Using artificial intelligence can help to analyze large quantities of data within a short time. The dataset shows are widespread landslides in the region are and how they might have been underestimated in their size and number in landslide inventories.

The Landslide prediction raster dataset contains the original landslide prediction (pixel value = 1).Landslide CNN prediction polygons cleaned, is the shapefile where contiguous landslide pixels are transformed to polygons, but removed depending on their location relative to water bodies, the ocean and human settlements.

Identifier
DOI https://doi.org/10.1594/PANGAEA.935704
Related Identifier References https://doi.org/10.1016/j.epsl.2022.117642
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.935704
Provenance
Creator Schönfeldt, Elisabeth ORCID logo; Korup, Oliver ORCID logo; Pánek, Tomáš; Winocur, Diego
Publisher PANGAEA
Publication Year 2021
Funding Reference Deutsche Forschungsgemeinschaft, Bonn https://doi.org/10.13039/501100001659 Crossref Funder ID STR 373/34-1 StRATEGy
Rights Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/
OpenAccess true
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 18 data points
Discipline Earth System Research
Spatial Coverage (-69.000 LON, -46.500 LAT)