Vehicle detection from airborne LiDAR point clouds based on a decision tree algorithm with horizontal and vertical features

ABSTRACT The object-based point cloud analysis (OBPCA) method has been used for vehicle detection from airborne light detection and ranging (LiDAR) point clouds with a relatively simple process and exhibits a degree of accuracy as high as that of a three-dimensional point cloud-based detection scheme. However, it only utilizes horizontal features of the segmented point clouds, and it uses thresholds established by heuristic observation and experience. In this article, we present a novel method for vehicle detection from airborne LiDAR point clouds based on a decision tree algorithm with horizontal and vertical features. It calculates the horizontal and vertical features for segments created by the filtering and segmentation processes, and it establishes a vehicle detection model by training a decision tree classifier with horizontal and vertical features of the segments. Our experiment shows that our proposed method outperforms the previous method in terms of recall and precision by 13.14% and 30.02%, respectively.