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This paper investigates using large vision-language models for built environment reasoning tasks, such as design suggestions and risk identification, leveraging remote sensing imagery. It evaluates models like InternVL and Qwen, highlighting their potential for supporting smart city decision-making and quantitative reasoning.
This research introduces the Housing Potential Common Data Model (HPCDM) to integrate diverse datasets for housing analysis and demonstrates its application through a City Digital Twin pilot.