Fundamental practices for drone remote sensing research across disciplines
Bibliographic record
Abstract
Drone remote sensing research has surged over the last few decades as the technology has become increasingly accessible. Relatively easy-to-operate drones put data collection directly in the hands of the remote sensing community. While an abundance of remote sensing studies using drones in myriad areas of application (e.g., agriculture, forestry, and geomorphology) have been published, little consensus has emerged regarding best practices for drone usage and incorporation into research. Therefore, this paper synthesizes relevant literature, supported by the collective experiences of the authors, to propose ten fundamental practices for drone remote sensing research, including (1) focus on your question, not just the tool, (2) know the law and abide by it, (3) respect privacy and be ethical, (4) be mindful consumers of technology, (5) develop or adopt a data collection protocol, (6) treat Structure from Motion (SfM) as a new form of photogrammetry, (7) consider new approaches to analyze hyperspatial data, (8) think beyond imagery, (9) be transparent and report error, and (10) work collaboratively. These fundamental practices, meant for all remote sensing researchers using drones regardless of area of interest or disciplinary background, are elaborated upon and situated within the context of broader remote sensing research.
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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 | 0.251 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.080 |
| Scholarly communication | 0.030 | 0.023 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".