The Application of Structure from Motion Photogrammetry to the Ontario Rockfall Hazard Rating System (RHRON)
Bibliographic record
Abstract
Roadways are considered critical infrastructure in North America, however, many pass-through hazardous terrains, such as rock slopes. To better manage and prioritize rockfall mitigation measures on rock slopes, the Ontario Rockfall Hazard Rating System (RHRON) was developed (Franklin et al., 2015). The RHRON prioritizes slopes into three Classes A, B, and C, with Class A being the most hazardous and warranting a further detailed investigation. The RHRON’s detailed investigation is more time-consuming and potentially hazardous as it subjects inspectors to roadway and rockfall hazards while making subjective slope assessments. This study aims to assess the integration of analysis of 3D point cloud data derived from Structure from Motion (SfM) photogrammetry from both terrestrial and mobile sources into the RHRON assessment procedure. The first part of the thesis investigates the applicability of SfM photogrammetry using cameras mounted on a vehicle. Several tests are conducted on camera settings, camera lenses, camera orientation, and image exposures. These tests are carried out on sites near Kingston, Ontario, with the objective of determining the possible bounds of quality of imagery to be input into SfM photogrammetry software, such as Agisoft Metashape. The second part of this thesis investigates the components of the RHRON rating system that can be derived from photogrammetry point cloud models. A repeatable and robust methodology of working with the data is presented using the open-source software CloudCompare. This methodology establishes best practices for the assessment of the RHRON parameters in a 3D virtual environment. Finally, a sensitivity analysis is conducted on the previously presented methodology by analyzing six photogrammetry models and one terrestrial laser scan model to determine the sensitivity of the RHRON method to poor-quality 3D models. The contributions in this thesis (i) enhance current rockfall hazard analysis practices by introducing a more objective methodology for RHRON assessments, (ii) provides a repeatable methodology to assess rock slopes from 3D models, and (iii) provides a recording mechanism for RHRON assessments as seen by the inspector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".