Multi-Source Earth Observation and Explainable GeoAI for Urban Flood Damage and Land-Impact Mapping DATA

Urban flooding increasingly threatens urban land, critical infrastructure, and essential services as climate change, rapid urbanization, and soil sealing intensify hydrometeor-ological risk. Timely post-disaster land and infrastructure mapping is therefore essen-tial for identifying exposed areas, assessing urban land vulnerability, and supporting emergency response, territorial planning, recovery, and resilience strategies. This study presents an integrated framework that fuses Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data through a hybrid CNN-LSTM architecture enhanced with explainable artificial intelligence (XAI). The framework was evaluated using 20 flood events recorded by the Copernicus Emergency Management Service between 2021 and 2024, representing more than USD 102 billion in economic losses. Fusion of RGB, NIR, and SWIR bands with SAR data achieved an intersection-over-union of 0.7053, corre-sponding to a 20.3% improvement over individual modalities. SAR contribution was strongly associated with the damage index in densely built-up areas, whereas optical information provided greater precision in peri-urban land. SHAP analysis identified Sentinel-2 SWIR bands B11 and B12 as the most influential variables for detecting damage in dense urban environments, followed by VV-polarized SAR backscatter, while NDVI and MNDWI showed greater importance in vegetated peri-urban zones. Grad-CAM maps spatially localized the image regions driving damage classifications, particularly affected buildings, road corridors, and critical-service areas, thereby im-proving the physical interpretability of model predictions. The proposed framework strengthens transparent post-flood damage assessment and provides actionable in-formation for land-use planning, infrastructure recovery, risk reduction, and urban re-silience.