用时空视觉变压器提升校园热应激高精度预测
Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments
- 融合时空视觉变压器与数字孪生，实现校园尺度热应力建模
- 基于德克萨斯州校园数据，生成高分辨率人体热感知预测
- 为城市气候适应性设计提供可落地的决策支持
极端高温事件在气候变化加剧下对城市韧性与规划构成严峻挑战。本研究提出一种气候响应型数字孪生框架，集成时空视觉变压器（ST-ViT）模型，以提升热应力预测与决策能力。以德克萨斯州某校园为测试场景，结合物理模拟、空间数据与气象信息，构建细粒度人体热感知预测。基于ST-ViT的数字孪生系统为规划者与利益相关方提供高效数据驱动洞察，支持精准热缓解策略制定，推动气候适应性城市设计发展。此次校园尺度示范为未来在更广泛多元城市环境中的应用奠定基础。
原文摘要 · Abstract (English)
Extreme heat events, exacerbated by climate change, pose significant challenges to urban resilience and planning. This study introduces a climate-responsive digital twin framework integrating the Spatiotemporal Vision Transformer (ST-ViT) model to enhance heat stress forecasting and decision-making. Using a Texas campus as a testbed, we synthesized high-resolution physical model simulations with spatial and meteorological data to develop fine-scale human thermal predictions. The ST-ViT-powered digital twin enables efficient, data-driven insights for planners and stakeholders, supporting targeted heat mitigation strategies and advancing climate-adaptive urban design. This campus-scale demonstration offers a foundation for future applications across broader and more diverse urban contexts.
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