Segmentation of individual tree crowns in colour aerial photographs using region growing supported by fuzzy rules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A method based on region growing for segmentation of tree crowns in aerial photographs is presented. By using a decision function, for including a pixel or not, in the spatial domain and in the colour domain simultaneously, the irregular contour of the tree crowns is kept in the segmentation result. Thus, contour information may be subsequently used for tree species classification. A set of possible candidate regions for each tree is evaluated, and the best region, according to a measure, is selected. The method is evaluated on a large sample of high spatial resolution aerial images in central Sweden. Almost 15 ha of natural regenerated boreal forests are included in the image material. On average, the identification of 73% of the tree crowns is in agreement with the corresponding results from the manual delineations. The estimation of the total number of stems from the 30 test images is 93% of the value obtained from the manual count.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it