Assessing Urban Safety: A Digital Twin Approach Using Streetview and Large Language Models

This study explores a novel approach to reevaluating urban safety using Vision Large Language Models (VLLMs) integrated with digital twin technology. Our methodology involves randomly selecting street views across various U.S. cities and employing VLLMs to detect and analyze street safety. We incorporate existing user-reported data through an API to validate our findings. Preliminary results indicate that this new approach significantly enhances the accuracy and reliability of urban safety assessments. The integration of VLLMs with digital twin frameworks presents a promising avenue for urban planners and policymakers to achieve more dynamic and real-time insights into city safety, ultimately contributing to smarter and safer urban environments. Our findings suggest that this method holds substantial potential for broader applications in digital twin initiatives, facilitating more informed decision-making processes.