Void-Volume-Based Stem Geometric Modeling and Branch-Knot Localization in Terrestrial Laser Scanning Data
Bibliographic record
Abstract
A comprehensive 3-D structural mapping of stem is essential for an accurate 3-D crown modeling and tree parameter estimation. Terrestrial laser scanning (TLS) is an effective technology for a comprehensive collection of individual tree level data, compared to destructive and costly field measurements. The performance of 3-D stem modeling techniques is adversely affected by laser shadowing and point-density variations in TLS data. In addition, most of the state-of-the-art techniques perform stem modeling using regular geometric shapes, such as circle, ellipse, and cylinder, which cannot accurately capture the complex 3-D stem geometric shapes. This results in stem modeling errors. In this article, we propose a 3-D stem modeling approach for both single- and multiscan TLS data that: 1) does not make any prior assumption on the stem geometry and 2) is minimally affected by undesirable stem shadowing and point-density variations. The proposed approach accurately models 3-D stem and localizes branch-knots for both coniferous and deciduous trees by mapping the void volume formed within the stem due to the opaqueness of tree stem to laser. The modeling performance was evaluated on both single- and multiscan data obtained for pine, spruce, and birch species. The low estimation errors associated with the stem diameter at breast height and branch-knot location, compared to the reference methods, prove the ability of the proposed method to both accurately model 3-D stem and localize branch-knots.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".