Sentinel-1 SAR-based Globally Distributed Co-Seismic Landslide Detection by Deep Neural Networks
Citation
Burrows, K., Milledge, D., Walters, R.J. (2024). Sentinel-1 SAR-based Globally Distributed Co-Seismic Landslide Detection by Deep Neural Networks. Geoscientific Model Development Discussions, preprint. Link to paper
Abstract
This research integrates Deep Neural Networks with SAR backscatter data for co-seismic landslide detection, utilizing a data-centric approach. The models were trained using 11 earthquake-induced multiple landslide events, covering approximately 73,000 landslides across diverse geologic and climatic settings. The top-performing model achieved a test F1-score of 82% in rapid assessment, representing significant progress in automated landslide detection. Testing on unseen events in Haiti (2021) and Sumatra (2022) demonstrates robust transferability, achieving F1-scores up to 82%. The approach harnesses cloud computing resources of Google Earth Engine for Sentinel-1 SAR image acquisition and processing, providing a valuable resource for rapid and comprehensive all-weather global landslide assessment.