Explainable Artificial Intelligence for Estimating Surface Deformation in Landslide Areas with Incomplete SAR Data

Abstract

In landslide-prone areas, spatial gaps in InSAR-derived deformation maps caused by incomplete SAR coverage hinder continuous surface deformation assessment and limit reliable landslide analysis. To address this problem, we propose an explainable AI (XAI) framework that integrates SBAS-InSAR, ensemble machine learning, and Shapley Additive exPlanations (SHAP) to estimate surface deformation in SAR-scarce regions. Geological and engineering factors, including protective measures, distance to roads, and land use, were combined with remote sensing and field data to build a comprehensive dataset. Four ensemble models (LightGBM, XGBoost, Random Forest, and CatBoost) were trained and evaluated, with XGBoost achieving the best performance (R² = 0.816, RMSE = 6.85 mm, MAE = 4.27 mm). Validation against two GNSS benchmarks confirmed sub-millimeter accuracy (0.6 mm and 0.3 mm). Both XGBoost and CatBoost delineated continuous deformation patterns consistent with field-observed damage. SHAP analysis provided model interpretability, highlighting elevation and human-engineering factors as key drivers: areas farther from roads and under cultivation were more prone to downslope movement, while damaged protective works exhibited greater deformation. By coupling InSAR with XAI, this study achieves accurate and interpretable surface deformation estimation in data-scarce regions, advancing landslide assessment and early warning applications.