Individual Tree Crown Delineation Based on Deep Learning for Arid Areas Using High-Resolution Satellite Imagery
Bibliographic record
Abstract
In this work, we explored the potential of the individual tree crown (ITC) delineation and individual tree species classification using deep learning using a Worldview-3 scene covering the Wushen Banner area, Ordos City, where is a typical arid region located in the hinterland of the Mu Us Desert. Combined with field survey data, an ITC delineation sample set and an individual tree species sample set were established. The Mask R-CNN model was employed for ITC delineation, and ResNet-18, GoogLeNet, and DenseNet-40 network models were considered for individual tree species classification. The results showed that the highest accuracy of ITC delineation using MASK R-CNN reached 78.9%. The training accuracies of individual tree species classification models were above 90%, and ResNet-18 achieved the highest classification accuracy. The trained ResNet-18 model was employed to classify tree species from the ITC delineation results of two testing images, and the overall accuracies were higher than 90%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".