AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASES
Keywords: Dense matching, Building detection, Classification, Digital Terrain Model, openstreetmap, point cloud
Abstract. In this paper a method of detecting buildings in dense populated city areas using a three-dimensional model, produced by aerial images, is described. Further to the detection of the outline of the building, we exact information about the buildings height. The study area is the wider centre of Athens, Greece. Our aim is to exact 3D information for large area, in minimum time and minimum cost, in order to support opensource data bases, such as openstreetmap.org. The proposed methodology consists of three main stages. In the first part of the procedure, aerial images are used to produce a point cloud, using the Semi-Global dense matching algorithm. Following, we classify the objects in the point cloud by remote sensing and photogrammetric methods. The classification’s results are divided in three main classes: ground, vegetation and buildings. Having detected the buildings and their complexes we attempt to find the outlines of each separate building, depending on its level; different levels are considered as different buildings. After detecting individual buildings in the point cloud, a polygon is created around their outline. All polygons were compared to the building polygons available on openstreetmap.org, in order to evaluate the results. The number of levels of 100 buildings, in different parts of the city, was measured manually in order to evaluate the Z-dimension’s results, and openstreetmap.org was updated with that information. Further update and combination of the database created in the current process, with the one available on openstreetmap.org is yet under study.