Remotely piloted aircraft systems and forests: a global state of the art and future challenges
Bibliographic record
Abstract
Remotely piloted aircraft system (RPAS) platforms are able to optimize the process of acquiring aerial images and improve the quality of the products generated in terms of spatial and temporal resolution. The exponential advance of the use of RPAS platforms in forestry, especially from the year 2010, is noteworthy. In this review, we present the global state of the art of the development and applications of RPAS technology in forestry, structured from a systematic review. Our results reveal a trend towards the use of multirotor RPAS platforms compared with fixed-wing platforms and that sensors that register in the visible spectral range are still the most widely used. More recent research has shown applications geared especially for areas such as forest inventory, with many innovations based on the detection of individual trees. Special focus has also been given to new alternatives for pest and disease mapping and phenological phenomena that occur at short intervals, as well as the monitoring of fires and postharvest areas. Therefore, there is a great potential for the use of RPAS platforms in a wide range of forest applications, whether linked to the productive sector or to the conservation of biodiversity, with great advances for spatiotemporal forest monitoring and expectations of further progress for the coming years.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".