Evaluating the Feasibility of ChatGPT for Mapping Building Attributes

With increasing rates of urbanization, many challenges are emerging regarding urban sustainability such as the energy usage of buildings. Coinciding with this is the growing attention of urban climate models for energy demand estimation and climate adaptation strategies. However, the applicability of these models is constrained by the lack of detailed urban surface information. Therefore, creating comprehensive datasets that capture urban surface information at a granular scale is crucial for responding to our rapidly urbanizing world. Recent advancements in Multimodal Large Language Model (MLLMs) have opened new opportunities in urban studies, offering accessible methods for information extraction. In this chapter we explore the feasibility of ChatGPT to extract building attributes from images. Taking New York City as a case study, we collect building images from Street View Imagery and process them through ChatGPT by posing specific questions to extract building attributes (e.g., height, functions, age). These attributes are then compared with authoritative data. The proposed method helps address the current dearth of fine-grained surface data on urban issues, therefore enhancing the accuracy and utility of urban climate models. Overall, this study demonstrates the practical applications of ChatGPT in geographic knowledge extraction, advancing the understanding of MLLMs in geographic contexts, and more broadly to the discourse on Artificial Intelligence (AI) in urban modeling and climate science.