A REVIEW OF UAV PHOTOGRAMMETRY APPLICATION IN ASSESSING SURFACE ELEVATION CHANGES
Bibliographic record
Abstract
Assessing large scale topography and surface elevation changes usually requires temporal remote sensing and aerial photogrammetrically-based data. Usually, very high resolution (VHR) elevation data, for instance, digital surface models (DSM) from light detection and ranging (LiDAR) and unmanned aerial vehicles (UAV), are used to visualise the changes in surface elevation. Therefore, this study attempts to review different case studies of assessing topography and surface elevation changes, specifically on geomorphological change detection (GCD) based on UAV photogrammetry data. The case study areas at Five Finger Strand (NW Ireland), Rosolina Mare (Italy) and Elbow River, Alberta (Canada) were discussed. This paper reviews the theory of UAV, surface elevation changes, and the summary of a related case study of surface elevation changes. The finding can provide a deep understanding of UAV applications in assessing surface elevation changes using UAV photogrammetry data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".