用多标签分类精准识别飓风后航拍图中的多种损毁类型
Multi-Label Classification Framework for Hurricane Damage Assessment
- 结合ResNet与特定类别注意力机制，一次识别多类损伤
- 在Rescuenet数据集上达到90.23%的平均精度，优于现有方法
- 适合灾害评估、城市应急响应与韧性规划领域使用
飓风造成广泛破坏，导致多种损伤类型和严重程度，需及时准确评估以支持有效灾后响应。传统单标签分类方法难以捕捉灾后损伤的复杂性，本文提出一种基于航拍图像的新型多标签分类框架。该方法融合基于ResNet的特征提取模块与类别特定注意力机制，可在单张图像中识别多种损伤类型。在飓风迈克尔的Rescuenet数据集上，该方法实现90.23%的平均精度，显著优于现有基线方法。该框架提升了灾后损伤评估能力，有助于制定更精准高效的应急响应策略，并为未来灾害减缓与韧性建设提供支持。本文已被ASCE国际土木工程计算会议（i3CE 2025）接收，最终版将刊载于官方会议论文集。
原文摘要 · Abstract (English)
Hurricanes cause widespread destruction, resulting in diverse damage types and severities that require timely and accurate assessment for effective disaster response. While traditional single-label classification methods fall short of capturing the complexity of post-hurricane damage, this study introduces a novel multi-label classification framework for assessing damage using aerial imagery. The proposed approach integrates a feature extraction module based on ResNet and a class-specific attention mechanism to identify multiple damage types within a single image. Using the Rescuenet dataset from Hurricane Michael, the proposed method achieves a mean average precision of 90.23%, outperforming existing baseline methods. This framework enhances post-hurricane damage assessment, enabling more targeted and efficient disaster response and contributing to future strategies for disaster mitigation and resilience. This paper has been accepted at the ASCE International Conference on Computing in Civil Engineering (i3CE 2025), and the camera-ready version will appear in the official conference proceedings.
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