Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".