Fine-Scale Spatial and Spectral Clustering of UAV-Acquired Digital Aerial Photogrammetric (DAP) Point Clouds for Individual Tree Crown Detection and Segmentation
Bibliographic record
Abstract
The field of remote sensing is undergoing rapid changes through the utilization of unmanned aerial vehicle (UAV) technology. The rise of this new technology and the corresponding growth in the application of digital aerial photogrammetric point clouds (DAP) require renewed investigation into individual tree detection (ITD) routines; most of which have been developed for airborne laser scanning (ALS) and traditional aerial imagery. This article analyzes the application of a well-known ALS-ITD routine to UAV-acquired DAP. In particular, specific modifications are proposed and evaluated aimed at improving its applicability to DAP, with particular emphasis on the incorporation of spectral information through subcrown scale k-means clustering. The new routine, which utilizes point-level spectral information in the clustering process, improved overall true positive detection by ~6.3%, with the most significant improvements in true positive detection found in lower canopy class stems. The new routine was also tested without the inclusion of spectral information and was shown to produce poorer results by ~4.1%, indicating that the inclusion of these data is beneficial for ITD approaches. This new routine represents an advancement of processing approaches incorporating both structural and spectral components for ITD in the era of UAV remote sensing in forested environments.
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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".