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This is a Observational study (also classified as: In Vitro Study).

August 21, 2025Open Access

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

Puntos clave

The FloodsNet model improved flood detection accuracy with an F1-score 1–2% higher than five other deep learning models.
Based on 16 flood events, the model used 32 Sentinel-1 images for training, validation, and detection tasks across the Yangtze River Basin.
The approach employed semi-automated inundation detection to generate 5296 labeled flood samples, enhancing mapping efficacy.
This study marks a significant advance in applying deep learning methods to large-scale flood monitoring using SAR technology.

Resumen

Synthetic Aperture Radar (SAR) technology offers unparalleled advantages by delivering high-quality images under all-weather conditions, enabling effective flood monitoring. This capability provides massive remote sensing data for flood mapping, while recent rapid advances in deep learning (DL) offer methodologies for large-scale flood mapping. However, the full potential of deep learning in large-scale flood monitoring utilizing remote sensing data remains largely untapped, necessitating further exploration of both data and methodologies. This paper presents an innovative approach that harnesses convolutional neural networks (CNNs) with Sentinel-1 SAR images for large-scale inundation detection and dynamic flood monitoring in the Yangtze River Basin (YRB). An efficient CNN model entitled FloodsNet was constructed based on multi-scale feature extraction and reuse. The study compiled 16 flood events comprising 32 Sentinel-1 images for CNN training, validation, inundation detection, and flood mapping. A semi-automatic inundation detection approach was developed to generate representative flood samples with labels, resulting in a total of 5296 labeled flood samples. The proposed model FloodsNet achieves 1–2% higher F1-score than the other five DL models on this dataset. Experimental inundation detection in the YRB from 2016 to 2021 and dynamic flood monitoring in the Dongting and Poyang Lakes corroborated the scheme’s outstanding performance through various validation procedures. This study marks the first application of deep learning with SAR images for large-scale flood monitoring in the YRB, providing a valuable reference for future research in flood disaster studies. This study explores the potential of SAR imagery and deep learning in large-scale flood monitoring across the Yangtze River Basin, providing a valuable reference for future research in flood disaster studies.

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