用图注意力网络提升航拍洪水区识别精度
Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas
- 将图注意力与切比雪夫层融入U-Net结构
- 在测试集上达到91% mAP、94% Dice分数
- 适合灾害应急与智慧城市规划使用
近年来，人类活动引发的气候变化和无序城市化导致洪灾频发。准确识别洪水区域对灾后管理与城市规划至关重要。尽管已有研究尝试使用卷积神经网络和基于Transformer的语义分割技术分析航拍影像中的洪水区域，但图神经网络的最新进展为改进提供了新机遇。本文提出一种创新模型——图注意力卷积U-Net（GAC-UNET），基于图神经网络实现洪水区域的自动识别。该模型在U-Net架构中引入图注意力机制和切比雪夫层，并探索了迁移学习与模型重编程对分割精度的提升效果。实验结果表明，所提GAC-UNET模型在性能上优于其他方法，mAP达91%，Dice分数为94%，交并比（IoU）为89%，为洪水易发区的科学决策与基础设施规划提供有力支持。
原文摘要 · Abstract (English)
The increasing impact of human-induced climate change and unplanned urban constructions has increased flooding incidents in recent years. Accurate identification of flooded areas is crucial for effective disaster management and urban planning. While few works have utilized convolutional neural networks and transformer-based semantic segmentation techniques for identifying flooded areas from aerial footage, recent developments in graph neural networks have created improvement opportunities. This paper proposes an innovative approach, the Graph Attention Convolutional U-NET (GAC-UNET) model, based on graph neural networks for automated identification of flooded areas. The model incorporates a graph attention mechanism and Chebyshev layers into the U-Net architecture. Furthermore, this paper explores the applicability of transfer learning and model reprogramming to enhance the accuracy of flood area segmentation models. Empirical results demonstrate that the proposed GAC-UNET model, outperforms other approaches with 91\% mAP, 94\% dice score, and 89\% IoU, providing valuable insights for informed decision-making and better planning of future infrastructures in flood-prone areas.
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