Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments

Wenjing Gong, Xinyue Ye, Keshu Wu, Suphanut Jamonnak, Wenyu Zhang +2 more
2/12/2025
cs.CV

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Abstract

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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Cite this paper

@article{gong2025integrating,
  title  = {Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments},
  author = {Wenjing Gong and Xinyue Ye and Keshu Wu and Suphanut Jamonnak and Wenyu Zhang and Yifan Yang and Xiao Huang},
  year   = {2025},
  eprint = {2502.09657},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.09657v2}
}

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