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<p>Flood disasters exert profound global impacts on socioeconomic systems and ecosystems, creating an urgent need for high-quality datasets to support flood detection. However, existing datasets remain constrained by limited source diversity, annotation precision, and scale. Sen2GF3Floods is introduced as the first dataset that integrates pre-disaster Sentinel-2 optical imagery with post-disaster Gaofen-3 SAR imagery. A dual-temporal collaborative annotation framework was constructed, combining semi-automatic labeling with active learning based on U-Net++ to achieve an effective balance between accuracy and efficiency. The dataset contains 21,483 standardized samples from nine major flood events, covering diverse geomorphological settings. Benchmark evaluations using five semantic segmentation models including U-Net, U-Net++, DeepLabV3+, DANet, and SegFormer indicate that multi-source fusion of Sentinel-2 RGB and NIR data with GF-3 HH and HV data delivers robust flood mapping performance across varied scenarios. Active learning reduces annotation costs while maintaining quality, and models trained on GF-3 SAR demonstrate good transferability to Sentinel-1 SAR. These results establish Sen2GF3Floods as a valuable resource for advancing flood detection algorithms and supporting operational and real-time disaster response.</p>

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Sen2GF3Floods: A Benchmark Multi-Source Flood Dataset with Dual-Temporal and Active Learning Annotation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月26日

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  • Wenting Chen,
  • Yueqin Zhu,
  • Wenlong Han,
  • Diyou Liu,
  • Guiquan Mo,
  • Ziyao Xing

摘要

Flood disasters exert profound global impacts on socioeconomic systems and ecosystems, creating an urgent need for high-quality datasets to support flood detection. However, existing datasets remain constrained by limited source diversity, annotation precision, and scale. Sen2GF3Floods is introduced as the first dataset that integrates pre-disaster Sentinel-2 optical imagery with post-disaster Gaofen-3 SAR imagery. A dual-temporal collaborative annotation framework was constructed, combining semi-automatic labeling with active learning based on U-Net++ to achieve an effective balance between accuracy and efficiency. The dataset contains 21,483 standardized samples from nine major flood events, covering diverse geomorphological settings. Benchmark evaluations using five semantic segmentation models including U-Net, U-Net++, DeepLabV3+, DANet, and SegFormer indicate that multi-source fusion of Sentinel-2 RGB and NIR data with GF-3 HH and HV data delivers robust flood mapping performance across varied scenarios. Active learning reduces annotation costs while maintaining quality, and models trained on GF-3 SAR demonstrate good transferability to Sentinel-1 SAR. These results establish Sen2GF3Floods as a valuable resource for advancing flood detection algorithms and supporting operational and real-time disaster response.