Comparative Analysis of Machine Learning and Deep Learning Models for Individual Tree Structure Segmentation Using Terrestrial LiDAR Point Cloud Data

This study aims to segment individual tree structures (stem, crown, and ground) from terrestrial LiDAR-derived point cloud data (PCD) and to compare the segmentation accuracy between two models: XGBoost (machine learning) and PointNet++ (deep learning). A total of 17 input features were categorized into spatial coordinates and normals, geometric structure features, and local distribution features. These were combined into four input configurations and evaluated under three downsampling conditions (2048, 4096, and 8192 points), resulting in 12 experimental setups. XGBoost achieved the highest stem segmentation F1-score of 87.8% using all features with 8192 points, whereas Point-Net++ reached 92.1% using only spatial coordinates and normals with 4096 points. The analysis of missegmentation patterns showed that XGBoost frequently confused structures near stem-to-ground boundaries and around branch junctions, while PointNet++ occasionally missegmented complex regions between stems and crowns. Regarding processing time, XGBoost required 10 to 47 min across all conditions, whereas Point-Net++ required 49 min for the 2048-point condition and up to 168 min for 8192 points. Overall, XGBoost provided advantages in computational efficiency and in generating feature-importance scores, while PointNet++ outperformed XGBoost in segmentation accuracy and the recognition of structurally complex regions.