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This project uses deep learning to semantically differentiate flood\u2010affected Chinese coastal regions using high\u2010resolution annotated images. Pixel\u2010by\u2010pixel water classification in RGB aerial images is done using an Efficient U\u2010Net architecture, which combines a pre\u2010trained EfficientNet encoder with a U\u2010Net decoder. The proposed model was trained and tested using the public Flood Area Segmentation dataset, which is assumed to represent Chinese coastal floods. Due to the short sample size, many data augmentation strategies were applied to increase model generalization. Experiments demonstrate the Efficient U\u2010Net provides good segmentation. Dice coefficient 0.88, F1 score 0.90, and Intersection over Union 0.82 were the final validation metrics. Qualitative research suggests that predicted flood masks match ground reality annotations. Results suggest deep neural networks might automatically and accurately track coastal floods. They also established a standard for multisensor research. Further research will examine how to use remote sensing to improve coastal flood detection and monitoring.<\/jats:p>","DOI":"10.1111\/tgis.70268","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T07:35:28Z","timestamp":1780990528000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Mapping of Coastal Flood Extents Using Deep Neural Networks and High\u2010Resolution Remote Sensing"],"prefix":"10.1111","volume":"30","author":[{"given":"Lin","family":"Zhang","sequence":"first","affiliation":[{"name":"Electronic Information and Electrical College of Engineering Shangluo University  Shangluo China"},{"name":"Shangluo Artificial Intelligence Research Center Shangluo University  Shangluo 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