Exploring transfer learning for individual tree species classification by cross-platform point cloud

Cross-platform Light Detection and Ranging (LiDAR) point clouds of tree species classification (TSC) from remains challenging due to substantial variations in point density, geometry, and noise across different LiDAR systems. Existing TSC studies are typically designed for a single data source and a limited number of species, which restricts their generalization to new acquisition platforms and previously unseen species. To address these challenges, we propose a learning framework that explicitly targets geometry only inputs, heterogeneous point densities, and limited labeled data in target domains. Specifically, we develop a compact preprocessing pipeline that augments geometric coordinates with local surface orientation and multi scale density descriptors, together with a sampling and normalization strategy that preserves informative local and global structure after subsampling. Furthermore, a multi-phase transfer learning strategy is introduced and verified to enable efficient adaptation from multi source pretraining to new sensors and species with minimal supervision. Experiments using the FOR-species20K dataset for pretraining and an unseen six-species ULS dataset of demonstrate that the proposed framework achieves an overall accuracy of and a mean F 1 -score of , exceeding models trained from scratch while converging up to 13-fold faster. These findings highlight the potential of transfer learning to enable rapid and accurate adaptation to new tree species, reducing data collection costs, and offering a scalable solution for cross-platform point cloud analysis for forest monitoring.