Detection and geometric characterization of rock mass discontinuities using a 3D high-resolution digital outcrop model generated from RPAS imagery – Ormea rock slope, Italy
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Bibliographic record
Abstract
The use of a remotely piloted aircraft system (RPAS) and digital photogrammetry is valuable for the detection of discontinuities in areas where field mapping and terrestrial photogrammetry or laser scanner surveys cannot be employed because the slope is unsafe, inaccessible, or characterized by a complex geometry with areas not visible from the ground. Using the Structure-from-Motion method, the acquired images can be used to create a 3D texturized digital outcrop model (TDOM) and a detailed point cloud representing the rock outcrop. Discontinuity orientations in a complex rock outcrop in Italy were mapped in the field using a geological compass and by manual and automated techniques using a TDOM and point cloud generated from RPAS imagery. There was a good agreement between the field measurements and manual mapping in the TDOM. Semiautomated discontinuity mapping using the point cloud was performed using the DSE, qFacet FM, and qFacet KD-tree methods applied to the same 3D model. Significant discrepancies were found between the semi-automatic and manual methods. In particular, the automatic methods did not adequately detect discontinuities that are perpendicular to the slope face (bedding planes in the case study). These differences in detection of discontinuities can adversely influence the kinematic analysis of potential rock slope failure mechanisms. We use the case study to demonstrate a workflow that can accurately map discontinuities with results comparable to field measurements. The combined use of TDOM and RPAS dramatically increases the discontinuity data because RPAS can supply a good coverage of inaccessible or hidden portions of the slope and TDOM is a powerful representation of the reality that can be used to map discontinuity orientations including those that are oriented perpendicular to the slope.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it