Automatic Building Detection from Lidar Point Cloud Data

This paper proposes an automatic system which detects buildings in urban and rural areas by the use of first pulse return and last pulse return LIDAR data. Initially both first and last pulse return points are interpolated to raster images. This results to two Digital Surface Models (i.e. DSM) and a differential DSM (i.e. DDSM) is computed by them. Then using a height criterion, rough and smooth regions of the DDSM are found. Then last pulse points lying inside smooth regions are filtered using a simplified Sohn filtering method to find the so called on-terrain points by which the Digital Terrain Model (i.e. DTM) is generated. The normalized DSM (i.e. nDSM) is calculated using first pulse-derived DSM and the calculated DTM. Afterwards two separated classifications are applied on the nDSM. The final results of classifications are a set of nDSM pixels belonging to building roofs. The accuracy of the proposed algorithm is evaluated using some metrics and has proved an overall accuracy of 95.1% and a correctness equal to 98.3% and a completeness factor equal to 89.5% which show the level of the efficiency and accuracy of the system.