Individual tree species prediction using airborne laser scanning data and derived point-cloud metrics within a dual-stream deep learning approach
Bibliographic record
Abstract
• Framework for automated generation of point-cloud segmentation and species labeling. • Novel dual-stream deep learning model for predicting tree species from point-clouds. • Comparative evaluation of point-based deep learning layers for species prediction. • Inclusion of point-cloud metrics in model improved accuracy by ∼ 11 % Accurate tree species mapping is essential for effective forest management but is often constrained by manual, labour-intensive workflows that limit scalability. While airborne laser scanning (ALS) supports large-scale forest attribute prediction, species classification remains difficult in complex, multi-species forests. To address this, we propose an automated, data-driven dual-stream deep learning framework that integrates ALS data with point-cloud metrics to identify individual tree species. Our framework incorporates an automated approach to individual tree segmentation and species labelling using existing forest inventory and field data, resulting in a dataset of 16,269 labelled individual tree point-clouds of four species across a 630,000 ha boreal mixed species forest in Ontario, Canada. Our dual-stream deep learning model integrates a Point Extractor to generate feature representations from raw ALS point-clouds and a complementary Metrics Network to process the point-cloud metrics. Results, based on the split test set of 2441 trees, showed that the inclusion of the Metrics Network improved tree species classification accuracy by approximately 11 % compared to models that rely solely on the Point Extractor. A weighted F1-score of 0.70 and area under the receiver operating characteristic curve of 0.88 was achieved using this dual-stream approach, along with enhanced predictive probabilities for all species thus improving the reliability of the predicted results. This approach reduces the manual processing bottleneck of individual tree segmentation and labelling and demonstrates the value of combining raw point-clouds and point-cloud metrics into a deep learning framework, offering a scalable and operational solution for reliable species predictions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".