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This paper presents a new segmentation technique for LIDAR point cloud data for automatic extraction of building roof planes. Usingthe ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups: ground and nongroundpoints. The ground points are used to generate a "building mask" in which the black areas represent the ground where there areno laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the buildingmask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individualbuilding or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations.Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to theappropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are small in size and randomlyoriented. Experimental results on the Vaihingen data set show that the proposed method offers high building detection and roof planeextraction rates

Rule-based segmentation of LIDAR point cloud for automatic extraction of building roof planes