Species Classification of Automatically Delineated Regenerating Conifer Crowns Using RGB and Near-Infrared UAV Imagery
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) and deep learning are important tools at the forefront of automated forest monitoring research, where classification of individual tree species is a critical forest management goal. Near-infrared (NIR) information provided by specialized UAV sensors may improve classification accuracy at the cost of added operational complexity; however, this potential for improvement is context-dependent and, therefore, may not be necessary. We assessed the performance of conventional red-green-blue (RGB) versus NIR imagery when classifying regenerating lodgepole pine and white spruce crowns automatically delineated by a trained deep learning algorithm. Models trained on NIR imagery slightly outperformed those trained on RGB imagery. Models trained on spectral bands outperformed those trained on spectral indices. The minor difference in performance between the two sets of imagery showed that accurate classification of lodgepole pine and white spruce can be carried-out using conventional RGB imagery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".