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

2025-02-12

AI Summary

To address the increasing frequency of extreme heat events on campuses under climate change, along with low spatiotemporal resolution and poor interpretability in thermal stress prediction, this paper proposes a climate-responsive digital twin framework. Methodologically, it introduces, for the first time, a spatiotemporal vision Transformer (ST-ViT) into a digital twin system, enabling end-to-end coupling between physics-based models (high-resolution CFD and urban canopy simulations) and deep learning, while integrating heterogeneous multi-source data—including meteorological, remote sensing, and geospatial datasets. The key contributions lie in overcoming longstanding bottlenecks in thermal environment modeling: achieving unprecedented spatial resolution (2 m × 2 m), dynamic temporal responsiveness (minute-level forecasting), and mechanistic interpretability. Validated on the University of Texas campus, the framework reduces thermal stress prediction error by 37%, enabling precise cooling interventions and sandbox simulation of adaptive urban design strategies.

Technology Category

• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Sustainability and climate impact of web technologies

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.

Problem

• Heat stress forecasting in urban areas
• Integration of Spatiotemporal Vision Transformer
• Climate-responsive digital twin framework

Innovation

• Spatiotemporal Vision Transformer
• Digital Twin Framework
• High-Resolution Heat Forecasting