Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks

Abstract. Rapid response to multiple landslide events demands accurate, all-weather, day-and-night detection capabilities. Optical remote sensing has advanced landslide detection but remains limited under adverse weather and lighting conditions. Synthetic Aperture Radar, resilient to these constraints, remains underexplored for automated landslide detection due to challenges such as complex pre-processing and geometric distortions. This study integrates Deep Neural Networks with Sentinel-1 backscatter data for co-seismic landslide detection, utilizing a data-centric approach. We train and test the models using 11 earthquake-induced widespread landslide events, covering ≈ 73 000 landslides across diverse geologic and climatic settings. Inference on unseen events in Haiti (2021) and Sumatra (2022) demonstrates robust transferability, achieving F1-scores up to 82 %. Using explainable artificial intelligence, we highlight the discriminative capability of change detection bands over backscatter alone. Our findings emphasize the potential of SAR-based DNN models for worldwide, generalized, and rapid landslide detection, addressing critical gaps in current methods that solely use optical data. This research lays a foundation for broader applications in automated SAR-based earth surface change detection, particularly in complex, hilly and mountainous terrains.