Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods
Bibliographic record
Abstract
Locating local maxima of grey levels in aerial images was used for individual tree detection in boreal, closed forest conditions in southern Finland. Image smoothing and binarization were used as preprocessing steps. Binarization was used to restrict the local maxima searching to the bright areas of the images, which were assumed to be tree crowns. Because brightness variations are typical of aerial images, both within and among images, locally adaptive methods were suggested for binarization. Aerial digital camera images and mapped tree data of eight stands in three field plots were used. Four adaptive binarization methods were compared. Differences in tree detection accuracy were small even though the appearance of the binarized images were different. Image smoothing improved the results of tree detection in the three stands that had the largest mean tree size. Tree detection worked fairly well in all seven stands with a density of less than 1500 trees/ha. In these stands, 7095% of the trees were detected, whereas only 54% were detected in the last stand, which had a density of approximately 1900 trees/ha.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".