Building Decay in Hong Kong: Assessing and Mapping Building Condition Index Using ChatGPT and Airborne Point Clouds

Hong Kong faces critical challenges in the maintenance and redevelopment of aged buildings. Recently, advancements in multi-modal generative AI (GenAI) and high-definition urban geospatial data, such as point clouds, have offered new opportunities to the architectural, engineering, and construction industry. This paper defines, assesses, and maps a Building Condition Index (BCI) as the condition of aged building fabrics using GenAI and high-definition geospatial data. First, a BCI is defined as a numerical scale of multi-dimensional factors, including floor area, building age, management quality, and the presence of unauthorized building works. Then, multiple data sources, including building exterior photos, airborne point clouds, and government building datasets, are processed and trained for the BCI using multiple regression and image embedding with ChatGPT4. Finally, a comprehensive BCI map and focused BCI hot spots can be visualized for an urban area. Experiments with over 1,200 building data points in Kowloon City, Hong Kong, indicated the robustness of the BCI in explaining the exogenous factors causing decayed buildings while accurately reflecting the building condition of buildings.