Detecting and mapping tree crowns based on convolutional neural network and Google Earth images
Bibliographic record
Abstract
Mapping tree crown is critical for estimating the functional and spatial distribution of ecosystem services. However, accurate and up-to-date urban crown mapping remains a challenge due to the time-consuming nature of field sampling and spatial heterogeneity. Another challenge is the data cost, which is always a concern for low-cost processing of forest maps on large scales. Here, we developed a novel working framework by integrating an advanced deep learning technology, the Mask Region-based Convolutional Neural Network (Mask R-CNN) model with Google Earth images to detect tree crown cover in New York’s Central Park, which is a typical testbed for an urban forest area with highly heterogeneous tree crown cover. The results indicated that the tree number detection rate estimated by the Mask R-CNN crown detection model was 82.8% and the crown area detection rate was 81.8% for the entire study area. The model detected isolated trees and closed forest trees areas with a recall of 87.5% and 81.6% of the tree numbers, respectively. The analysis indicates that the tree crown detection model could accurately detect tree crowns under highly complex environments and demonstrates great potential to map urban tree crown covers.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".