August 21, 2025Remote SensingOpen Access

Large-Scale Flood Detection and Mapping in the Yangtze River Basin (2016–2021) Using Convolutional Neural Networks with Sentinel-1 SAR Images

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Convolutional neural networks improve flood mapping accuracy in the yangtze river basin, suggesting new methodologies for expansive monitoring.

Flood mapping accuracy improved by the FloodsNet model, achieving 1–2% higher F1-score than five alternative models.
A total of 5296 labeled flood samples were generated from 16 flood events in sentinel-1 images for effective detection.
This approach utilizes synthetic aperture radar data in dynamic flood monitoring across the yangtze river basin.
The study highlights the untapped potential of deep learning and remote sensing data for large-scale flood studies.

Wu et al. (2025) studied this question.

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Convolutional neural networks improve flood mapping accuracy in the yangtze river basin, suggesting new methodologies for expansive monitoring.