Remotely piloted aircraft imagery for automatic tree counting in forest restoration areas: a case study in the Amazon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Throughout the world, restoration of degraded areas (RDA) is not only a global but also a local challenge. In this context, the Brazilian government committed itself to restore 12 million hectares of forests by 2030. RDA monitoring customarily depends on extensive fieldwork to collect data on all individuals planted. As remotely piloted aircrafts (RPAs) can reduce costs and time of fieldwork activities, studying this technology is therefore timely given. A crucial metric for RDA is the number of trees established in the area. Methods using RPAs on automatic tree counting showed good accuracy using algorithms based on the canopy height model (CHM), which is the difference between a digital surface model (DSM) and a digital terrain model (DTM). However, obtaining a DTM demands an extra computational processing step and may require field control points or manually delimiting objects on the surface. The study presented here proposes and evaluates a semi-automated methodology for counting trees directly on DSM in RDAs in the Amazon using RPA coupled with a red–green–blue standard photographic sensor. The DSM method obtained good overall accuracy and F-score indexes, superior to the CHM method for all study areas even when overall accuracy was low for both methods.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it