Tree crown segmentation based on a geometric tree crown model for prediction of forest variables
Bibliographic record
Abstract
A new algorithm for tree crown segmentation from airborne laser scanning data was validated at a test site in southern Sweden (lat. 58° N, long. 13° E). The tree crown segmentation algorithm used a correlation surface created by fitting a geometric tree crown model and was also controlled using an a priori probability function. If the model fit alone was used, 69% of the field-measured trees were detected but when a priori information was used, the proportion of detected trees increased to 75%. The proportion of detected trees represented 95% of the total stem volume for all field measured living trees. The tree crown segments, with zero, one, or several trees, were used as input to an imputation algorithm for prediction of forest variables, which yielded relative root mean square errors of 8.9% for stem volume, 7.2% for basal area, 3.8% for mean tree height, 6.3% for mean stem diameter, and 15% for stem density, after aggregation to plot level for cross-validation. Thus, automatic tree crown delineation using the segmentation algorithm could be used for imputation of tree stems to obtain high accuracy predictions of several forest variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".