Segment Anything Model-Based Building Footprint Extraction for Residential Complex Spatial Assessment Using LiDAR Data and Very High-Resolution Imagery

Abstract

With rapid urbanization, retrieving information about residential complexes in a timely manner is essential for urban planning. To develop efficiency and accuracy of building extraction in residential complexes, a Segment Anything Model-based residential building instance segmentation method with an automated prompt generator was proposed combining LiDAR data and VHR remote sensing images in this study. Three key steps are included in this method: approximate footprint detection using LiDAR data, automatic prompt generation for the SAM, and residential building footprint extraction. By applying this method, residential building footprints were extracted in Pukou District, Nanjing, Jiangsu Province. Based on this, a comprehensive assessment model was constructed to systematically evaluate the spatial layout of urban complexes using six dimensions of assessment indicators. The results showed the following: (1) The proposed method was used to effectively extract residential building footprints. (2) The residential complexes in the study area were classified into four levels. The numbers of complexes classified as Excellent, Good, Average, and Poor were 10, 29, 16, and 1, respectively. Residential complexes of different levels exhibited varying spatial layouts and building distributions. The results provide a visual representation of the spatial distribution of residential complexes that belong to different levels within the study area, aiding in urban planning.