Decision-relevant building damage assessment is critical for prioritizing resources and recovery after a disaster, yet most automated methods either flatten damage into a single severity scale (no damage, minor, major, destroyed) or require paired pre- and post-event imagery that is often unavailable for emerging hazards. This paper presents Damage-TriageFormer, a single-image, post-event, footprint-conditioned model that produces a damage typology rather than a severity scale. We contribute: (1) DamageTriage-Bench, a new benchmark built from NOAA Emergency Response Imagery across Hurricane Michael (2018), Hurricane Helene (2024), and the 2025 Los Angeles wildfire complex, with five typology classes that distinguish roof damage from structural damage and, within each, partial from total extent; and (2) Damage-TriageFormer, which extends a DINOv3 ViT-L backbone with a Simple Feature Pyramid for higher-resolution instance pooling, a two-stage gated damage head, and an auxiliary severity-regression objective. Our model achieves macro F1 of 0.624 on validation and 0.619 on a held-out stratified test set, performing strongest where operational triage needs it most, with per-class F1 of 0.91 and 0.84 on undamaged buildings and total structural collapse, respectively. While the rare Total Roof Damage class remains difficult due to its limited examples and an inherently ambiguous label boundary, our results show that single-image post-event imagery can support actionable building damage typing, enabling targeted emergency response and resource allocation without a pre-event reference.

翻译：决策相关的建筑损伤评估对于灾后资源优先配置与恢复至关重要，然而现有自动化方法要么将损伤扁平化为单一严重程度等级（无损伤、轻微、严重、摧毁），要么需要通常难以获得的灾前与灾后配对影像。本文提出Damage-TriageFormer，一种基于灾后单张影像与足迹约束的模型，其输出损伤类型而非严重程度等级。我们的贡献包括：（1）DamageTriage-Bench，一个基于NOAA应急响应影像构建的新基准数据集，覆盖2018年迈克尔飓风、2024年海伦飓风及2025年洛杉矶野火复合灾害，包含五种损伤类型：区分屋顶损伤与结构损伤，并进一步区分各自的局部与全面程度；（2）Damage-TriageFormer，其以DINOv3 ViT-L骨干网络为基础，扩展了用于更高分辨率实例池化的简单特征金字塔、两阶段门控损伤头及辅助严重程度回归目标。该模型在验证集上达到0.624的宏平均F1值，在留出分层测试集上达到0.619，在操作性分类最需要的场景中表现最强——无损坏建筑与完全结构倒塌的类别F1值分别为0.91和0.84。尽管罕见的“完全屋顶损伤”类别因样本有限及标签边界固有模糊性仍具挑战性，但研究结果表明，单张灾后影像可支持可操作的建筑损伤分类，无需灾前参考即可实现针对性应急响应与资源分配。