Article
Flood Region Segmentation Using SegNet Deep Neural Networks
Accurate and timely identification of flood-affected regions is critical for disaster response, emergency resource allocation, and damage assessment. This paper proposes a deep learning-based flood region segmentation system using an enhanced SegNet encoder-decoder architecture adapted for multi-source remote sensing image analysis. The proposed model is augmented with skip connections, attention gates, and multi-scale input processing to improve flood boundary delineation accuracy. The system achieves a mean IoU of 87.3% for flood region segmentation on the Copernicus Emergency Management Service dataset, outperforming baseline SegNet, U-Net, and FCN models. The framework produces pixel-wise flood maps within seconds, supporting rapid disaster response workflows with strong generalization across diverse geographic environments and flood event types.
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