A Segmentation-Based Multimodal Framework for Operational Landslide Mapping Using Post-Event SAR

Abstract. Rapid and reliable landslide mapping is critical for post-disaster response, yet Synthetic Aperture Radar (SAR)-based detection remains challenging due to speckle noise, geometric distortions, and complex terrain. This study develops an operational post-event landslide extraction framework using a UNet segmentation architecture with multimodal geospatial data fusion. High-resolution COSMO-SkyMed SAR imagery is combined with terrain representations derived from Digital Elevation Models (DEM), Red Relief Image Maps (RRIM), and rainfall indices to evaluate the contribution of complementary geospatial information to segmentation performance. Experiments were conducted across three major landslide-triggering events in Japan (Kyushu, Hokkaido, and Kumamoto), comparing SAR-only and multimodal configurations. Results demonstrate that integrating terrain information and rainfall data improves landslide detection performance compared with SAR-only inputs. RRIM consistently outperforms DEM as a topographic descriptor, particularly in steep or heterogeneous terrain, while rainfall information provides moderate gains in recall. Boundary-based metrics further indicate improved geometric fidelity of mapped landslides when multimodal inputs are incorporated. The framework requires only a single post-event SAR acquisition supplemented with publicly available ancillary datasets, enabling rapid and scalable generation of landslide inventories without reliance on pre-disaster imagery. These findings establish a reproducible baseline for SAR-driven landslide segmentation and highlight the potential of multimodal geospatial data fusion for operational disaster response and hazard monitoring.