Segmentation of Individual Trees in TLS Point Clouds via Graph Optimization
Bibliographic record
Abstract
Individual tree segmentation from terrestrial laser scanning (TLS) point clouds is essential for precise forest inventory, instance-level tree modeling, and the estimation of forest stock volume. However, current instance-level segmentation techniques encounter significant challenges in complex forest environments, particularly those characterized by dense understory vegetation and substantial crown overlap in natural forests. These complexities reduce segmentation accuracy and limit the generalizability of existing methods across diverse forest types. This paper presents a unified method for individual tree segmentation that integrates trunk localization with crown segmentation. The trunk localization uses normal vector features to eliminate non-trunk slice points, employs an enhanced DBSCAN algorithm for trunk slice separation, and refines trunk positions by fitting circular-like trunk slices using the Hough transform. This integrated approach ensures precise segmentation and optimization of final trunk positions. Subsequently, a graph-based optimization method is applied for crown segmentation. This method incorporates supervoxel technology, an optimal Euclidean distance metric between supervoxels, and a supervoxel similarity metric to construct an optimal undirected graph. Tree crown supervoxels are segmented by tracing the shortest path from the crown supervoxels to their corresponding tree roots. We validated the proposed method on eight sample plots representing various complexities and forest types. For tree trunk localization, the proposed method achieved an average Mean accuracy of 0.761, which is 27% higher than the best result among the three traditional methods. For crown segmentation, it achieved an average mIoU of 0.645, marking a 31% improvement over the best baseline performance. The source code for our individual tree segmentation method is available at https://github.com/TLS-tree/tree-segmentation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".