VOXEL-BASED POINT CLOUD SEGMENTATION AND BUILDING DETECTION
Keywords: Land registry maps, point cloud techniques, voxels, building detection, RANSAC, segmentation, classification, region enlargement
Abstract. In the recent years, point cloud technologies, such as Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanners (TLS), Aerial Laser Scanners (ALS), let alone Mobile Mapping Systems (MMS), have come into the focus of attention and have been a subject of considerable public concern in mapping. Thanks to these new techniques, experts can survey large areas with sufficient and homogenous accuracy with high resolution. It comes from this that there are several areas where the point clouds can be used. One of the possible applications of point clouds is updating land registry maps. Many countries worldwide face the issue that a significant part of their large-scale land registry maps are outdated and inaccurate. One of these countries is Hungary, where more than eighty percent of digital cadastre maps were digitised using analogous maps in a scale range of 1:1000 – 1:4000. In this paper, a novel processing queue is presented to find the footprints of the building. Our solution is based on primarily well-known algorithms (RANSAC, DBSCAN) implemented in open-source Python packages. An automated flow was developed, composed of simple processing steps, to cut the point cloud into wall and roof segments and vectorise the wall points under roofs into building footprints. The algorithms and Python programs were tested in villages where detached houses are typical. Tests were made on three study areas in Hungary and we achieved well-promising results.