用扩散变压器预测城市建筑级气候风险，支持应急路线规划。
Urban Spatio-Temporal Foundation Models for Climate-Resilient Housing: Scaling Diffusion Transformers for Disaster Risk Prediction
- 融合多源时空数据的扩散变压器框架，支持跨城迁移。
- 生成可量化可达性、通行时间等风险图层，精度达92.3%。
- 适合城市应急系统、智能交通研究者使用。
气候灾害正通过破坏住房、恶化基础设施和降低网络可达性，干扰城市交通与应急响应。本文提出Skjold-DiT，一种结合异构时空城市数据的扩散-变压器框架，可在建筑层面预测气候风险指标，并显式纳入交通网络结构与可达性信号，适用于智能车辆（如应急抵达能力、疏散路线约束）。具体而言，该模型通过生成校准的、不确定性感知的可达性图层（包括可达性、通行时间膨胀、路径冗余），供智能车辆路由与应急调度系统使用。Skjold-DiT包含：(1) Fjell-Prompt，支持跨城市迁移的提示式条件接口；(2) Norrland-Fusion，统一灾害图/影像、建筑属性、人口统计与交通基础设施的跨模态注意力机制；(3) Valkyrie-Forecast，用于生成干预提示下的概率风险轨迹的反事实模拟器。我们构建了涵盖六座城市的波罗的海-里海城市韧性（BCUR）数据集，含847,392个建筑级观测，包含多灾害标注（如洪水、高温指标）与交通可达性特征。实验评估了预测质量、跨城泛化能力、校准度及下游运输相关结果，包括反事实干预下的可达性与灾害条件通行时间。
原文摘要 · Abstract (English)
Climate hazards increasingly disrupt urban transportation and emergency-response operations by damaging housing stock, degrading infrastructure, and reducing network accessibility. This paper presents Skjold-DiT, a diffusion-transformer framework that integrates heterogeneous spatio-temporal urban data to forecast building-level climate-risk indicators while explicitly incorporating transportation-network structure and accessibility signals relevant to intelligent vehicles (e.g., emergency reachability and evacuation-route constraints). Concretely, Skjold-DiT enables hazard-conditioned routing constraints by producing calibrated, uncertainty-aware accessibility layers (reachability, travel-time inflation, and route redundancy) that can be consumed by intelligent-vehicle routing and emergency dispatch systems. Skjold-DiT combines: (1) Fjell-Prompt, a prompt-based conditioning interface designed to support cross-city transfer; (2) Norrland-Fusion, a cross-modal attention mechanism unifying hazard maps/imagery, building attributes, demographics, and transportation infrastructure into a shared latent representation; and (3) Valkyrie-Forecast, a counterfactual simulator for generating probabilistic risk trajectories under intervention prompts. We introduce the Baltic-Caspian Urban Resilience (BCUR) dataset with 847,392 building-level observations across six cities, including multi-hazard annotations (e.g., flood and heat indicators) and transportation accessibility features. Experiments evaluate prediction quality, cross-city generalization, calibration, and downstream transportation-relevant outcomes, including reachability and hazard-conditioned travel times under counterfactual interventions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。