Recognition and Possible Remediation of Automated Tree Delineations with Multiple Isolations per Tree (Split Cases) on High-Resolution Imagery
Bibliographic record
Abstract
Individual tree crown analysis from high-resolution imagery is gaining greater use in forest applications. Automated crown delineations (isols) that are poor can cause errors in species classification and inventory estimates. This study explores the issue of recognizing split cases (e.g., tree crowns oversegmented into several isols) and demonstrates 3 remediation procedures to improve delineations. Several methods for identifying split cases are proposed, but a conceptual framework for a template-matching approach is developed further. Candidate split cases are identified where there is a good match of a template model representing the appearance of trees with the imagery, and several isols are within the template. Candidates are further analyzed through evidence such as isol shape, species class, and match of templates centered on each isol. Procedures were demonstrated with a typical individual crown isolation on 40 cm multispectral imagery of a mixed species forest in northeastern Ontario. The process showed useful effectiveness in improving the isolation, with expected omission rates of 15%–20% and 25%–30% false alarms. Overall, almost all true split cases recognized had improved crown delineations. The work shows that approaches for recognizing and remediating split cases are possible, but will have to be complex and consider multiple evidence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".