用卫星图像分灾后建筑损毁类型，比单纯判损毁有无更利于精准救援
DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization
- 基于灾前灾后影像对，用分层U-Net与Transformer进行多类损伤分类
- 在四个损毁类别上达0.737 IoU和0.846 F1-score，跨飓风数据验证了可迁移性
- 适合需要精细损毁评估的应急响应、资源调配场景
灾后快速准确的建筑损毁评估对资源调度至关重要，但现有自动化方法通常仅提供二分类（损毁/未损毁）或有序严重度分级。本文提出DamageCAT框架，通过基于建筑类型的分类实现更细致的损毁评估。贡献包括：(1) 构建BD-TypoSAT数据集，包含飓风Ida前后卫星图像三元组，涵盖四类损毁——部分屋顶损毁、完全屋顶损毁、部分结构坍塌、完全结构坍塌；(2) 设计一种用于处理灾前灾后图像对的分层U-Net-Transformer架构。模型整体取得0.737 IoU和0.846 F1-score，在跨事件评估中展现出对飓风Harvey、Florence和Michael数据的迁移能力。尽管因类别不平衡导致各类别表现不均，该框架表明基于类型分类的评估方式比传统严重度分级更具行动指导意义，有助于实现精准应急响应与资源分配。
原文摘要 · Abstract (English)
Rapid, accurate, and descriptive building damage assessment is critical for directing post-disaster resources, yet current automated methods typically provide only binary (damaged/undamaged) or ordinal severity scales. This paper introduces DamageCAT, a framework that advances damage assessment through typology-based categorical classifications. We contribute: (1) the BD-TypoSAT dataset containing satellite image triplets from Hurricane Ida with four damage categories - partial roof damage, total roof damage, partial structural collapse, and total structural collapse - and (2) a hierarchical U-Net-based transformer architecture for processing pre- and post-disaster image pairs. Our model achieves 0.737 IoU and 0.846 F1-score overall, with cross-event evaluation demonstrating transferability across Hurricane Harvey, Florence, and Michael data. While performance varies across damage categories due to class imbalance, the framework shows that typology-based classifications can provide more actionable damage assessments than traditional severity-based approaches, enabling targeted emergency response and resource allocation.
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