INVESTIGATING THE IMPACT OF POINT CLOUD DENSITY ON SEMANTIC SEGMENTATION PERFORMANCE USING VIRTUAL LIDAR IN BOREAL FOREST

Virtual LiDAR Scan (VLS) serves as a powerful tool for the replication of real world conditions and can assist with the calibration of LiDAR systems. In this study, we utilize HELIOS++, a VLS software, to investigate the impact of point cloud density on the semantic segmentation performance of a well-established Deep Learning (DL) method for point clouds, KPConv. Our experiment is focused on a typical Quebec boreal forest composed of Abies balsamea and Picea mariana. We generated 10250 structurally diverse forest plots to train 10 DL models on a wide range point cloud densities to assess their effect on the semantic segmentation. Densities varied from 23 points/m(2) to 225 points/m(2), replicating point clouds output from classic airborne LiDAR scanning and high-density unmanned LiDAR scanning. Our results demonstrate that point cloud densification improves IoU score for both boreal tree species by an average of 0.3 percentage points per 10 points/m(2).