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

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.

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Cite this as

Schönfeldt, Elisabeth, Korup, Oliver, Pánek, Tomáš, Winocur, Diego (2021). Dataset: Giant landslide inventory of Patagonia classified with the convolutional neural network AlexNet. https://doi.org/10.1594/PANGAEA.935704

DOI retrieved: 2021

Additional Info

Field Value
Imported on November 29, 2024
Last update November 29, 2024
License CC-BY-4.0
Source https://doi.org/10.1594/PANGAEA.935704
Author Schönfeldt, Elisabeth
Given Name Elisabeth
Family Name Schönfeldt
More Authors
Korup, Oliver
Pánek, Tomáš
Winocur, Diego
Source Creation 2021
Publication Year 2021
Resource Type text/tab-separated-values - filename: Landslide_CNN_prediction_Patagonia
Subject Areas
Name: Geophysics

Related Identifiers
Title: Deep learning reveals one of Earth's largest landslide terrain in Patagonia
Identifier: https://doi.org/10.1016/j.epsl.2022.117642
Type: DOI
Relation: References
Year: 2022
Source: Earth and Planetary Science Letters
Authors: Schönfeldt Elisabeth , Winocur Diego , Pánek Tomáš , Korup Oliver .