Detecting and Updating Changes in Lidar Point Clouds for Automatic 3D Urban Cartography

Abstract. This work presents a method that automatically detects, analyses and then updates changes in LiDAR point clouds for accurate 3D urban cartography. In the proposed method, the 3D point cloud obtained in each passage is first classified into 2 main object classes: Permanent and Temporary. The Temporary objects are then removed from the 3D point cloud to leave behind a perforated 3D point cloud of the urban scene. These perforated 3D point clouds obtained from different passages (in the same place) at different days and times are then matched together to complete the 3D urban landscape by incremental updating. Different natural or man-made changes occurring in the urban landscape over this period of time are detected and analyzed using cognitive functions of similarity and the resulting 3D cartography is progressively modified and updated accordingly. The results, evaluated on real data using different standard evaluation metrics, not only demonstrate the efficacy of the proposed method but also shows that this method is easily applicable and well scalable, making it suitable for handling large urban scenes.