Estimation of Landslide Hazard Zones Using Deep Learning Based on Diverse Geospatial Data

Abstract: Traditional landslide hazard mapping in Japan relies on labor-intensive field surveys, which are slow, costly, and fail to update dynamically amid rising climate-driven disasters like the 2018 Heavy Rain Event, leaving gaps in timely evacuations. This study addresses these challenges by proposing a semantic segmentation framework using ResUNet to fuse Sentinel-2 optical, Sentinel-1 SAR amplitude, DEM-derived Terrain Ruggedness Index (TRI), and JAXA land cover data, tackling class imbalance with BCE + Dice loss and providing probability/uncertainty maps via 4-TTA for robust hazard delineation under adverse weather. The principal aim is to enable operational, weather-robust hazard zone extraction with AUC upto 0.89 (best multimodal configuration), outperforming single-modality baselines (e.g., optical-only AUC 0.74; SAR-only 0.69) through synergistic feature fusion, while highlighting multimodal SAR's edge for cloud-obscured scenarios. Validated on Hiroshima Prefecture data—Japan's highest-risk region with ~32,000 hazard spots—this approach demonstrates pre/post-disaster change detection, but reveals limitations in spatial generalization due to region-specific training.

Kohei Arai, Kengo Oiwane and Hiroshi Okumura. “Estimation of Landslide Hazard Zones Using Deep Learning Based on Diverse Geospatial Data”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.2 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170214

@article{Arai2026,
title = {Estimation of Landslide Hazard Zones Using Deep Learning Based on Diverse Geospatial Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170214},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170214},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {2},
author = {Kohei Arai and Kengo Oiwane and Hiroshi Okumura}
}