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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.09657 |
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| _version_ | 1866909797768495104 |
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| author | Gong, Wenjing Ye, Xinyue Wu, Keshu Jamonnak, Suphanut Zhang, Wenyu Yang, Yifan Huang, Xiao |
| author_facet | Gong, Wenjing Ye, Xinyue Wu, Keshu Jamonnak, Suphanut Zhang, Wenyu Yang, Yifan Huang, Xiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments Gong, Wenjing Ye, Xinyue Wu, Keshu Jamonnak, Suphanut Zhang, Wenyu Yang, Yifan Huang, Xiao Computer Vision and Pattern Recognition 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. |
| title | Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.09657 |