Deep Learning for Water Body Segmentation in Remote Sensing Imagery: A Review

Water body segmentation is essential for flood monitoring, water resource management, and ecological and environmental protection. Recent advances in deep learning and remote sensing have substantially improved the automation and accuracy of water body segmentation. However, existing methods remain susceptible to false positive predictions in complex landscapes containing shadows and dense vegetation. Their ability to delineate fragmented water surfaces and small water bodies is also limited, and their robustness to seasonal variations and transferability across regions require further improvement. This paper presents a comprehensive review of water body segmentation methods, datasets, evaluation metrics, current challenges, and future research directions. It examines representative models derived from U-Net, DeepLabv3+, Transformer, Mamba and the Segment Anything Model (SAM), with particular emphasis on their architectural modifications and improvement strategies. Representative datasets covering rivers, lakes, and reservoirs are summarized, and commonly used evaluation metrics are reviewed and interpreted. Future research should focus on spatiotemporal adaptive multimodal fusion, segmentation strategies for large model integration, fine-grained water body segmentation, and the integration of explainability and physical mechanisms. This review provides a useful reference for improving water body segmentation models, developing high-quality water body datasets, and supporting practical applications in water resource management and environmental monitoring.