Fundamental practices for drone remote sensing research across disciplines
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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 | 0.001 |
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 it