Exploring Transfer Learning for Individual Tree Species Classification by Cross-Platform Point Cloud
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".