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The method of building outline extraction based on segmentation of airborne laser scanning data is proposed and tested on a dataset comprising 1,400 buildings typical for residential and industrial urban areas. The algorithm starts with setting a special threshold to separate building from bare earth points and low objects. Next, local planes are fitted to each point using RANSAC and further refined by least squares adjustment. A normal vector is assigned to each point. Similarities among normal vectors are evaluated in order to assemble planar or curved roof segments. Finally, building outlines are formed from detected segments using the a-shapes algorithm and further regularized. The extracted outlines were compared with reference polygons manually derived from the processed laser scanning point cloud and orthoimages. Area-based evaluation of accuracy of the proposed method revealed completeness and correctness of 87 % and 97 %, respectively, for the test dataset. The influence of parameters like number of points per roof segment, complexity of the roof structure, roof type, and overlap with vegetation on accuracy was evaluated and discussed.

Comprehensive approach for building outline extraction from LiDAR data with accent to a sparse laser scanning point cloud