SilvaScenes: Tree Segmentation and Species Classification from Under-Canopy Images in Natural Forests
Bibliographic record
Abstract
Interest in forestry automation is growing alongside rapid advances in deep learning. In particular, tree detection and taxonomic classification are seen as core tasks required for automating field surveys and forestry equipment. These operations must often be performed in under-canopy settings, which pose challenging conditions for perception systems, including heavy occlusion, variable lighting, and dense vegetation. Despite this necessity, current work has yet to properly establish the feasibility of simultaneously executing tree detection and taxonomic classification in natural forests, as available datasets primarily focus on urban settings or on a limited number of species. To address this gap, we present SilvaScenes, a benchmark dataset for instance segmentation of tree species from under-canopy images in natural forests. Collected across five bioclimatic domains in Quebec, Canada, our dataset features 1421 trees from 28 species, with segmentation masks for pixel-precise tree trunk detection and fine-grained species annotations from forestry experts. We demonstrate the relevance and difficult nature of SilvaScenes by evaluating modern deep learning approaches, showing that while trunk segmentation is feasible, with a top mean average precision (mAP) of 69.9% and mean average recall (mAR) of 76.4%, species-aware segmentation remains a significant challenge with an mAP and an mAR of only 39.2% and 68.6%, respectively. Alongside additional experiments, we highlight key challenges, namely that species imbalance and tree occlusion figure among the most pressing issues for precise segmentation and identification. Meanwhile, higher image resolutions contribute to significant performance gains and will likely prove fundamental to these tasks moving forward. Our dataset, source code, and models will be made available at https://github.com/norlab-ulaval/SilvaScenes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".