A Density-Based Clustering Method for the Segmentation of Individual Buildings from Filtered Airborne LiDAR Point Clouds

Individual building segmentation is a prerequisite for building reconstruction. When building points or building regions are classified from raw LiDAR (Light Detection and Ranging) point clouds, the dataset usually contains numerous individual buildings as well as outliers. However, the applications to segment individual buildings from large datasets require the algorithms working with the minimal requirements of domain knowledge to determine the input parameters, working well on datasets with outliers and having good efficiency on big data. To meet these requirements, this paper presents a new segmentation method relying on a density-based clustering technique that is designed to separate individual buildings in dense built-up areas and is robust to outliers. As implemented in a spatial database, the algorithm benefits from the spatial index and the parallel computation capability offered by the system. The experimental results show that the proposed method is significantly more effective in segmenting individual buildings than the well-known moving window algorithm and the new boundary identification and tracing algorithm, and processes large volumes of data with good efficiency. Compared with the moving window algorithm, the proposed method (parallelized) consumed only 17.8% time and the quality improved from 88.8 to 94.8% on the Vaihingen dataset.