A robust segmentation framework for closely packed buildings from airborne LiDAR point clouds

Urban villages (UVs) are commonly found in many Asian cities. These villages contain many closely packed buildings constructed decades ago without proper urban planning. There is a need for those buildings to be identified and put into statistics. In this paper, we present a segmentation framework that invokes multiple machine learning techniques and point cloud/image processing algorithms to segment individual closely packed buildings from large urban scenes. The presented framework consists of two major segmentation processes. The framework first filters out the non-ground objects from the point cloud, then it classified them by using the Random Forest classifier to isolate buildings from the entire scene. After that, the building point clouds will be segmented based on several building attribute analysis methods. This is followed by using the Random Sample Consensus (RANSAC) plane filtering method to expand the space between two closely packed buildings, so that the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique can be used to more accurately segment each individual building from the closely packed building areas. Two airborne Light Detection and Ranging (LiDAR) datasets collected in two different cities with some typical closely packed buildings were used to verify the proposed framework. The results show that the framework can effectively identify the closely packed buildings with unified structures from large airborne LiDAR datasets. The overall segmentation accuracy reaches 84% for the two datasets. The proposed framework can serve as a basis for analysis and segmentation of closely packed buildings with a more complicated structure.