Automatic Segmentation of Lidar Data at Different Height Levels for 3d Building Extraction

This paper presents a new LiDAR segmentation technique for automatic extraction of building roofs. First, it uses a height threshold, based on the digital elevation model to divide the LiDAR point cloud into ‘ground’ and ‘non-ground’ points. Then starting from the maximum LiDAR height, and decreasing the height at each iteration, it looks for coplanar points to form planar roof segments. At each height level, it clusters the points based on the distance and finds straight lines using the points. The nearest coplanar point to the midpoint of each line is used as a seed and the plane is grown in a region growing fashion. Finally, a rule based procedure is followed to remove planar segments in trees. The experimental results show that the proposed technique offers a high building detection and roof plane extraction rates while compared to a recently proposed technique.