Classification of raw LiDAR point cloud using point-based methods with spatial features for 3D building reconstruction

Abstract

Building extraction from light detection and ranging (LiDAR) data for 3-dimensional (3D) reconstruction requires accurately classified LiDAR points. In recent years, approaches developed for the classification mostly based on gridded LiDAR data. In the gridding process of LiDAR data, there is a characteristic point loss which results in reduced height accuracy. The effect of such loss can be eliminated using classified raw LiDAR data. In this study, an automatic point-based classification approach for raw LiDAR data classification with spatial features has been proposed for 3D building reconstruction. Using spatial features, the hierarchical rules have been determined. The spatial features, such as height, the local environment, and multi-return, of the LiDAR points were analyzed, and every single LiDAR points automatically assigned to the classes based on these features. The proposed classification approach based on raw LiDAR data had an overall accuracy of 79.7% in the test site located in Istanbul, Turkey. Finally, 3D building reconstruction was performed using the results of the proposed automatic point-based classification approach.