图注意力卷积U-NET：用于识别洪水区域的语义分割模型
Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas
AI总结：
本文提出图注意力卷积U-NET模型，结合图注意力机制和Chebyshev层，用于航空影像中洪水区域的自动识别，以91% mAP、94% Dice和89% IoU优于现有方法。
摘要：
近年来，人为气候变化和未经规划的城市建设影响日益加剧，导致洪水事件频发。准确识别洪水区域对于有效的灾害管理和城市规划至关重要。尽管已有少量工作利用卷积神经网络和基于Transformer的语义分割技术从航空影像中识别洪水区域，但图神经网络的最新进展创造了改进的机会。本文提出了一种创新方法，即基于图神经网络的图注意力卷积U-NET（GAC-UNET）模型，用于自动识别洪水区域。该模型将图注意力机制和Chebyshev层集成到U-Net架构中。此外，本文还探讨了迁移学习和模型重编程在提高洪水区域分割模型准确性方面的适用性。实验结果表明，所提出的GAC-UNET模型以91%的mAP、94%的Dice分数和89%的IoU优于其他方法，为洪水易发地区的明智决策和未来基础设施的更好规划提供了宝贵见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
