Evaluation Of Uav Photogrammetric Accuracy For Mapping And Earthworks Computations
Bibliographic record
Abstract
This study quantifies the accuracies achieved and tests the validity of an in-house developed Unmanned Aerial Vehicle (UAV) system employed in a stockpile volumetric survey. UAV photogrammetric results are compared with conventional GNSS survey results. To test the repeatability of the UAV system, multiple flights were flown over the same stockpile using different GNSS ground control, at different times and weather conditions. Positional accuracies of UAV photogrammetric results were found to be very similar to those from GNSS RTK survey, at the scale of photography flown. UAV stockpile volume results agreed with those from GNSS within 3 755 m3 (0.7%) on a 530 255 m3 pile. Stockpile volume comparisons between subsequent UAV surface models agreed within 877 m3 (0.2%) on the same pile. Geometric analysis of independent UAV photogrammetric models over the same area indicated that they could be considered the same at a 95% confidence level. We conclude that the UAV photogrammetric approach is, at the very least, equivalent in accuracy to GNSS RTK surveys at the scale of photography observed. The accuracy of the UAV photogrammetric surveys were sufficient for 1:200 scale mapping and 0.145 m contours. The UAV photogrammetric approach also provided greater detail, resulting in more representative models of the measured surfaces.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".