Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments
This work addresses urban resilience and planning challenges for planners and stakeholders by providing data-driven insights for heat mitigation strategies, though it is incremental as it builds on existing digital twin and transformer methods.
The study tackled high-resolution heat stress forecasting in campus environments by integrating a Spatiotemporal Vision Transformer into a digital twin framework, resulting in fine-scale human thermal predictions for a Texas campus testbed.
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.