Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges
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
The recent development and proliferation of unmanned aircraft systems (UASs) has made it possible to examine environmental processes and changes occurring at spatial and temporal scales that would be difficult or impossible to detect using conventional remote sensing platforms. This review article highlights new developments in UAS-based remote sensing, focusing mainly on small UASs (<25 kg). Because this class is generally less expensive and more versatile than larger systems the use of small UASs for civil, commercial, and scientific applications is expected to expand considerably in the future. To highlight different environmental applications, we provide an overview of recent progress in remote sensing with small UASs, including photogrammetry, multispectral and hyperspectral imaging, thermal, and synthetic aperture radar and LiDAR. We also draw on the literature and our own research experience to identify some key research challenges, including limitations of the current generation of platforms and sensors, and the development of optimal methodologies for processing and analysis. While much of the potential of small UASs for remote sensing remains to be realised, it is likely that the next few years will see such systems being used to provide data for an ever-increasing range of environmental applications.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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