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

• URL: http://arxiv.org/abs/2502.09657v1
• Date: Wed, 12 Feb 2025 05:27:16 GMT
• Title: Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments
• Authors: Wenjing Gong, Xinyue Ye, Keshu Wu, Suphanut Jamonnak, Wenyu Zhang, Yifan Yang, Xiao Huang,
• Abstract summary: This study introduces a climate-temporal digital twin framework to enhance heat stress forecasting and decision-making.<n>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.
• Score: 14.46777973030641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, policymakers, and campus stakeholders, supporting targeted heat mitigation strategies and advancing climate-adaptive urban design.

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