Drones and Geography: Who Is Using Them and Why?
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
Drones have equipped geographers with the capacity to collect high-quality geospatial data at multiple spatial, spectral, and temporal resolutions. Although the adoption of drones is increasing across geography, knowledge of those using this technology and their practices is limited. The purpose of this article is to understand who is using drones in geography, how they are using them, and what future opportunities exist. We collected data from eighty-eight survey respondents, predominantly based in the United States but a handful from Australia, Canada, the European Union, and the United Kingdom. The findings from our Web-based survey show that about 85 percent of geographers using drones are White. Female respondents made up only about 30 percent of respondents, although they represented about 75 percent of the eighteen to twenty-four age group. Although the word drone has a negative connotation, most users (∼38 percent) prefer it, followed by unmanned aerial vehicle (∼21 percent) and unmanned aerial systems (∼19 percent). Only 22 percent of geographers have more than six years of drone experience, suggesting the rapid growth in use and popularity among geographers. Off-the-shelf drones are the most desirable, perhaps due to their low cost and ease of use. Overall, drones in geography are considered positive and have introduced a new era of small extent geospatial analyses.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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