Edge-guided dual-branch network for flood mapping in satellite imagery: a deep learning approach

Accurate flood extent mapping plays a pivotal role in water resource assessment, hydrological modeling, and emergency response. This study develops Edge-Guided Attention Fusion Flood Net (EAFFNet), a deep learning model for precise flood extent extraction from satellite imagery. EAFFNet combines spatial feature extraction with semantic interpretation - its spatial branch captures directional features while the semantic branch models long-range pixel relationships. The integrated edge enhancement and attention fusion modules effectively address semantic overlap issues, enabling accurate segmentation of complex flood boundaries. The comparative results on two open-sourced datasets, the Sen1Floods11 and GF-FloodNet datasets, indicated that the proposed model achieved mean Intersection over Union (mIoU) scores of 41.45% and 85.05%, respectively, which perform better than other advanced deep learning models. As a whole, we believe the proposed model can perform well in flood monitoring and provide a reliable technical solution for disaster early-warning systems.