Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia - Miao Sun; Tan Qin; Yuanxiao Kuang; Jianchang Lv (Journal of Asian Architecture and Building Engineering)
Industrial buildings are an important spatial resource and play a crucial role in sustainable urban regeneration of high-density post-industrial metropolitan in Asia with insufficient spatial resources. This paper develops a machine learning method for identifying industrial buildings from satellite aerial images. It extracts vector footprints of buildings from aerial imagery through image segmentation, establishes a feature engineering model comprising 11 distinct indicators, and introduces a Random Forest model to enhance the analysis. By mining the implicit spatial design requirements present in geographical information, this methodology facilitates the classification of industrial buildings from hundreds of thousands of buildings. The results demonstrate that the identification of typical characteristics can discover the scale, distribution, and surrounding built environment of industrial buildings in Shanghai’s Central City, providing valuable data for managing industrial spatial resources from lot to building granularity, implementing a systematical and comprehensive re-planning, and popularizing adaptive reuse strategy, with the goal of leading a shift in policies and paradigms of urban regeneration for improvement of efficiency, balance, and green transformation in high-density post-industrial metropolitan in Asia.