The Use of Unmanned Aerial Vehicles (UAVs) in Slope Stability Assessment
Bibliographic record
Abstract
The use and applications of unmanned aerial vehicles (UAVs) in geotechnical engineering is rapidly growing, leading to changes in the way that data is acquired, analyzed and processed. UAVs can reach areas previously inaccessible via ground or helicopter, while also being quickly deployed. Cameras are the current standard for data collection and 3D model creation. There are multiple types of UAV’s currently available. Quadcopters can take off and land in spatially constrained areas, but carry a small stabilized camera producing low quality models. Octocopters permit an increased payload, so a higher quality camera can be attached, allowing for increased model accuracy. Flight time is reduced by the additional weight. Fixed wing UAVs create higher quality photogrammetry models, and are commonly deployed over large surface areas. Transport Canada certification must be approved prior to any flights occurring for research or work. A detailed application must be created, including a flight plan and demonstration of prior flight experience. At the White Canyon site in B.C., a Phantom 4 Quadcopter was flown for geotechnical analysis of a complex geometry slope, which has previously been studied for several years. The terrain has occluded the data available from the ground or from permissible helicopter flight paths. Therefore, detailed information from the slope has not been previously available. The process of using a UAV to obtain these data sets, to develop a full 3D model of these areas of the slope is discussed, considering the accuracy and quality of the data available.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".