Explainable, Confidence-Mapped Deep Learning for Remote-Sensing–Driven Landslide Susceptibility Mapping

Landslide susceptibility mapping is widely used for risk reduction, yet many high-performing deep models remain hard to interpret and rarely communicate where predictions are reliable. We present an explainable, confidence-mapped workflow that combines remote sensing/GIS-derived conditioning layers with modern deep tabular architectures (FT-Transformer, ResMLP, and TabNet). To test the developed methodology, a case-study area in the Regional Unit of Magnesia (Zagora–Mouresi, Greece) was selected. Conditioning factors describing terrain, hydrology, proximity, and geology are Frequency Ratio–weighted, then used to train probabilistic susceptibility models evaluated with discrimination and calibration metrics. Spatial confidence is mapped using normalized predictive entropy to identify zones where susceptibility estimates are less decisive. Explainability is achieved with SHapley Additive exPlanations (SHAP), consistently highlighting elevation as the dominant control, followed by aspect, with lithology and slope also exerting strong influence; proximity to the river network and faults and curvature-related metrics contribute secondarily. The resulting susceptibility and confidence products improve transparency for decision support and provide a scalable template for large-area hazard assessment.