Scholarlink

<p>Rapid and precise flood extent mapping is vital for effective disaster response, as floods cause infrastructure damage, crop losses, casualties, and major economic disruptions. Conventional hydrological models and remote sensing approaches often suffer from high computational demands, coarse spatial resolution, and limited real-time adaptability. This study offers a systematic comparison of three state-of-the-art deep learning segmentation architectures (UNet, SegNet, and DeepLabV3+), each paired with either EfficientNet or ResNet backbones. Our goal is to identify the most efficient and accurate model for flood-affected region delineation. In experiments, UNet with EfficientNet backbone outperformed alternatives, achieving an accuracy of 0.9705, a precision of 0.9609, a recall of 0.9548, a F1-score of 0.9575 and an IoU of 0.9271. These results highlight EfficientNet’s strong feature extraction and lightweight design, making it ideal for near real-time operational deployment. Overall, our findings offer a practical, scalable solution for real-world disaster management, enabling faster damage assessment, optimized resource allocation, and improved flood response.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning based flood segmentation: evaluating EfficientNet and ResNet for UNet, SegNet and DeepLabV3+

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

查看全文
  • Şeyda Karcı,
  • Elif Özceylan Aslan,
  • Kazım Yıldız,
  • Önder Demir

摘要

Rapid and precise flood extent mapping is vital for effective disaster response, as floods cause infrastructure damage, crop losses, casualties, and major economic disruptions. Conventional hydrological models and remote sensing approaches often suffer from high computational demands, coarse spatial resolution, and limited real-time adaptability. This study offers a systematic comparison of three state-of-the-art deep learning segmentation architectures (UNet, SegNet, and DeepLabV3+), each paired with either EfficientNet or ResNet backbones. Our goal is to identify the most efficient and accurate model for flood-affected region delineation. In experiments, UNet with EfficientNet backbone outperformed alternatives, achieving an accuracy of 0.9705, a precision of 0.9609, a recall of 0.9548, a F1-score of 0.9575 and an IoU of 0.9271. These results highlight EfficientNet’s strong feature extraction and lightweight design, making it ideal for near real-time operational deployment. Overall, our findings offer a practical, scalable solution for real-world disaster management, enabling faster damage assessment, optimized resource allocation, and improved flood response.