Laser-scanned tree stem filtering for forest inventories measurements
Bibliographic record
Abstract
With specific flora and fauna, regional landscapes and forests constitute an important part of the cultural heritage. Several natural environments have already been classified as national or regional parks. The UNESCO World Heritage covers 13% of the protected forests in the world. Thus, preserving those sites represents a crucial issue. Such a safeguarding involves a detailed knowledge of the sites and forestry management plans. The management of a natural forest is traditionally based on forest plot inventories in which several features of the trees are measured. The set of data collected during these inventories represents the starting point of forest monitoring, flora preservation and risks prevention. Traditionally, measurements are made manually by operators. However, during the last decade, terrestrial laser scanning has become a new and promising way of measuring such attributes. This instrument provides a fine three dimensional point cloud virtual representation of the scanned scene. Trees location, stem diameter, and stem taper can be extracted from these point clouds using pattern recognition algorithms. In this paper we present a novel two steps way to improve the quality of tree branching detection in a three dimensional point cloud acquired by terrestrial laser scanner. This method was developed in order to enhance the results of a previous study. Our approach is based on the combination of a simplification step (using particle simulation), followed by a shape detection (discrete arcs of circle detection). It identifies the lack of accuracy in tree stem diameter measurements at branching junctions for further more detailed analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".