Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models

ArXi:2605.08404v1 Announce Type: cross This work investigates the use of large language models (LLMs) for tasks in smart cities. The core idea is to leverage remote sensing imagery to characterize the built environment, including design suggestions, constructability assessment, landuse patterns, and risk identification. We examine remote sensing imagery at multiple spatial scales as inputs for multimodal language modeling and evaluate their effects on built-environment-related reasoning.