Giant landslide inventory of Patagonia classified with the convolutional neural network AlexNet
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| Format: | Dataset Open Access |
| Langue: | en |
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PANGAEA
2021
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| _version_ | 1867168212850835456 |
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| author | Schönfeldt, Elisabeth Korup, Oliver Pánek, Tomáš Winocur, Diego |
| author_facet | Schönfeldt, Elisabeth Korup, Oliver Pánek, Tomáš Winocur, Diego |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | 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. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_935704 |
| institution | PANGAEA |
| language | en |
| publishDate | 2021 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Giant landslide inventory of Patagonia classified with the convolutional neural network AlexNet Schönfeldt, Elisabeth Korup, Oliver Pánek, Tomáš Winocur, Diego Binary Object; Binary Object (File Size); Convolutional neural network; File content; Landslide detection; Landslide inventory; Landslides; NatHazGr; Natural Hazards Group; Patagonia; Patagonia_region; StRATEGy; StRATEGy international research training group 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. |
| title | Giant landslide inventory of Patagonia classified with the convolutional neural network AlexNet |
| topic | Binary Object; Binary Object (File Size); Convolutional neural network; File content; Landslide detection; Landslide inventory; Landslides; NatHazGr; Natural Hazards Group; Patagonia; Patagonia_region; StRATEGy; StRATEGy international research training group |
| url | https://doi.org/10.1594/PANGAEA.935704 |