Multi-scale three-dimensional detection of urban buildings using aerial LiDAR data

Extraction of urban objects, and analysis of two- and three-dimensional (2D and 3D) morphological parameters, as well as 2D and 3D landscape metrics in urban environments are valuable for updating GIS databases and for civic applications, urban planning, disaster risk assessment, and climate and sustainability studies. However, few studies have reported the extraction of 3D buildings at different scales in urban areas or developed a method for accuracy evaluation. This study aims at developing a method of multi-scale 3D building information extraction (MS3DB) to fill this research gap. Surface flatness and the variance in the normal direction were extracted from the point cloud data, and gray-level co-occurrence matrix was extracted from normalized digital surface models as labeling features, which were fused into a graph-cut the algorithm to determine building labeling. In addition, 2D and 3D building morphological parameters were extracted at the grid-scale, and a set of 3D building landscape metrics were designed at the city-block scale. Accuracy of the extraction of 3D building information was evaluated at the object, grid, and block scales. The model achieved high accuracy in extracting the building labels using data from the northern part of Brooklyn, New York, USA. The results show that the MS3DB method yields limited accuracy in extracting the building edges, whereas other parameters (e.g., area, volume, and planar area index) were extracted with high accuracy at the grid scale (R-2 > 0.92). The block-scale landscape analysis shows the advantages of integrating 2D and 3D features (e.g., differences in the vertical landscape) in characterizing the structure of urban buildings and exhibits moderate accuracy (R-2 > 0.79).