Multi-Resolution Data Fusion for Resilient Flood Mapping

Floods are among the most destructive and costly natural disasters worldwide, necessitating accurate and timely mapping for emergency response and impact assessment. Remote sensing offers a valuable means to monitor inundation, yet trade-offs in spatial, spectral, and temporal resolution limit single-sensor approaches. This study presents a comparative analysis of flood mapping using multimodal sensor fusion and semantic segmentation. We integrate high-resolution UAV RGB imagery with Synthetic Aperture Radar (SAR) data from Sentinel-1 and UAVSAR, focusing on the classification of four land cover classes relevant to flooding: inundated vegetation, dry vegetation, open water, and others. A modified U-Net architecture was developed to handle multimodal stacked inputs, allowing systematic evaluation of different sensor combinations. The results show that the combination of RGB UAV with UAVSAR VV polarization at 5 m resolution achieves the highest performance (mean IoU: 84. 9%), outperforming RGB UAV alone (77. 8%) and significantly surpassing the combinations of higher resolution UAVSAR (1 m, 61. 2%) and Sentinel-1. These findings highlight the complementary strengths of optical and SAR features, the importance of contextual information, and the counterintuitive benefit of medium-resolution UAVSAR for scene-level understanding. Incorporation of SAR indices such as VH/VV ratios provided modest gains, particularly in vegetation-rich areas. Cross-domain testing with Hurricane Matthew imagery revealed a notable drop in generalization (best mean IoU: 46.2%), underscoring challenges of domain shift and the need for domain-adaptive modeling. Overall, the results emphasize the advantages of multimodal fusion, especially UAV RGB with UAVSAR VV (5 m), for fine-grained flood mapping and suggest future directions in multi-temporal and adaptive frameworks for robust flood monitoring.