Forest information extraction from high spatial resolution images using an individual tree crown approach
Bibliographic record
Abstract
RESUME iv INTRODUCTION 1 TECHNIQUES AND METHODS 2 IMAGE PREPROCESSING AND MASKS GENERATION 4 INDIVIDUAL TREE CROWN DELINEATION 6 INDIVIDUAL TREE CROWN CLASSIFICATION 10 ACCURACY ASSESSMENTS 11 INDIVIDUAL TREE CROWN REGROUPING AND STAND GENERATION 12 THE TREE-TOP APPROACH 14 FOREST INVENTORY APPLICATIONS 16 CONCLUSION 22 REFERENCES 23 Cover Image – Results of species classification and regrouping of individual tree crowns and tree clusters over the original panchromatic IKONOS image (1 m/pixel) for part of a 10 000 ha area (11.7 x 8.6 km2) in the Lac a lʼOurs region of Quebec that was analyzed with the individual tree crown approach. This work was done in collaboration with CLC-Camint (Gatineau) and Industries Davidson Inc. and was funded in part by the “Programme de mise en valeur des ressources du milieu forestier Volet 1” of the Quebec Department of Natural Resources. The tree species in the forested areas are indicated by the following colours:
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".