A New Fast and Low-Cost Photogrammetry Method for the Engineering Characterization of Rock Slopes
Bibliographic record
Abstract
Digital photogrammetry (DP) represents one of the most used survey techniques in engineering geology. The availability of new high-resolution digital cameras and photogrammetry software has led to a step-change increase in the quality of engineering and structural geological data that can be collected. In particular, the introduction of the structure from motion methodology has led to a significant increase in the routine uses of photogrammetry in geological and engineering geological practice, making this method of survey easier and more attractive. Using structure from motion methods, the creation of photogrammetric 3D models is now easier and faster, however the use of ground control points to scale/geo-reference the models are still required. This often leads to the necessity of using total stations or Global Positioning System (GPS) for the acquisition of ground control points. Although the integrated use of digital photogrammetry and total station/GPS is now common practice, it is clear that this may not always be practical or economically convenient due to the increase in cost of the survey. To address these issues, this research proposes a new method of utilizing photogrammetry for the creation of georeferenced and scaled 3D models not requiring the use of total stations and GPS. The method is based on the use of an object of known geometry located on the outcrop during the survey. Targets located on such objects are used as ground control points and their coordinates are calculated using a simple geological compass and trigonometric formula or CAD 3D software. We present three different levels of survey using (i) a calibrated digital camera, (ii) a non-calibrated digital camera and (iii) two commercial smartphones. The data obtained using the proposed approach and the three levels of survey methods have been validated against a laser scanning (LS) point cloud. Through this validation we highlight the advantages and limitations of the proposed method, suggesting potential applications in engineering geology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 0.003 |
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".