Integrating InSAR and Channel Steepness for AI-Based Coseismic Landslide Modeling in the Nepal Himalaya

Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose major threats to life, infrastructure, and post-disaster recovery. Although coseismic landslide susceptibility mapping increasingly uses machine learning (ML) and deep learning (DL), explicit integration of spaceborne interferometric synthetic aperture radar (InSAR) products, particularly line-of-sight (LOS) displacement and coherence-based damage proxy maps (DPM), remains limited in event-based frameworks. This study develops and evaluates a multi-factor coseismic landslide probability model that incorporates InSAR-derived deformation metrics with key geomorphic and hydrologic predictors to improve rapid post-earthquake hazard assessment. Using the 25 April 2015 Mw 7.8 Gorkha earthquake as a case study, LOS displacement was derived from ALOS-2 PALSAR-2 ScanSAR interferometry, and the normalized channel steepness index (Kₛₙ) was computed from a digital elevation model. Additional predictors included slope, aspect, curvature, elevation, drainage density, distance to river, log-transformed stream power index (logSPI), peak ground acceleration (PGA), rainfall, and land use/land cover. Five models: Random Forest, Extreme Gradient Boosting (XGBoost), a lightweight convolutional neural network, U-Net, and DeepLabV3 were trained using fourteen conditioning factors and a landslide inventory, with class imbalance addressed through majority undersampling for ML and weighted loss with patch oversampling for DL. Incorporating LOS and DPM improved model discrimination and calibration: XGBoost and Random Forest achieved the highest AUC-ROC values (0.972 and 0.969) and lowest Brier scores, while DeepLabV3 produced the highest AUC-PR (0.768) and CSI (0.49). Feature importance analysis identified Kₛₙ as the dominant predictor, and ablation tests confirmed the added value of InSAR metrics. These findings demonstrate the effectiveness of integrating InSAR products for rapid coseismic landslide hazard assessment in the Nepal Himalaya.