Living in a changing climate, human society now faces more frequent and severe natural disasters than ever before. As a consequence, rapid disaster response during the "Golden 72 Hours" of search and rescue becomes a vital humanitarian necessity and community concern. However, traditional disaster damage surveys routinely fail to generalize across distinct urban morphologies and new disaster events. Effective damage mapping typically requires exhaustive and time-consuming manual data annotation. To address this issue, we introduce Smart Transfer, a novel Geospatial Artificial Intelligence (GeoAI) framework, leveraging state-of-the-art vision Foundation Models (FMs) for rapid building damage mapping with post-earthquake Very High Resolution (VHR) imagery. Specifically, we design two novel model transfer strategies: first, Pixel-wise Clustering (PC), ensuring robust prototype-level global feature alignment; second, a Distance-Penalized Triplet (DPT), integrating patch-level spatial autocorrelation patterns by assigning stronger penalties to semantically inconsistent yet spatially adjacent patches. Extensive experiments and ablations from the recent 2023 Turkiye-Syria earthquake show promising performance in multiple cross-region transfer settings, namely Leave One Domain Out (LODO) and Specific Source Domain Combination (SSDC). Moreover, Smart Transfer provides a scalable, automated GeoAI solution to accelerate building damage mapping and support rapid disaster response, offering new opportunities to enhance disaster resilience in climate-vulnerable regions and communities. The data and code are publicly available at https://github.com/ai4city-hkust/SmartTransfer.

翻译：在气候变化的背景下，人类社会如今面临比以往更频繁更严重的自然灾害。因此，在搜救的“黄金72小时”内实现快速灾害响应，成为至关重要的人道主义需求和社区关注焦点。然而，传统的灾害损伤调查通常难以泛化至不同城市形态及新型灾害事件。有效的损伤制图往往需要详尽且耗时的人工数据标注。为解决此问题，我们提出智能迁移（Smart Transfer）——一种新颖的地理空间人工智能（GeoAI）框架，利用最先进的视觉基础模型（FMs），基于震后甚高分辨率（VHR）影像实现建筑物快速损伤制图。具体而言，我们设计了两种新颖的模型迁移策略：其一，像素级聚类（PC），确保稳健的原型级全局特征对齐；其二，距离惩罚三元组（DPT），通过向语义不一致但空间邻近的图块施加更强惩罚，整合图块级空间自相关模式。基于近期2023年土耳其-叙利亚地震的广泛实验与消融研究，展示了在多种跨区域迁移设定（即留一域外推（LODO）与特定源域组合（SSDC））下的优越性能。此外，智能迁移提供了一种可扩展、自动化的GeoAI解决方案，以加速建筑物损伤制图并支持快速灾害响应，为提升气候脆弱区域与社区的灾害韧性提供了新机遇。相关数据与代码已在 https://github.com/ai4city-hkust/SmartTransfer 公开。