Comparison of airborne laser scanning, terrestrial laser scanning, and terrestrial photogrammetry for mapping differential slope change in mountainous terrain
Bibliographic record
Abstract
Traditional mapping and monitoring of active slope processes in mountainous terrain is challenging, given often difficult site accessibility, obstructed visibility, and high complexity of the terrain. For example, the rockfall hazard evaluation system employed by Canadian railways relies partly on visibility of the rockfall source zone from track level, which is often impossible for large or complex slopes, in the mountains and elsewhere. Recent advancements in remote sensing, data collection, and analysis algorithms have helped resolve some of these issues by allowing the slope processes to be mapped, and thereby understood, with a greater degree of accuracy and confidence than was previously possible. For example, a better understanding of the rate of movement of material around a natural rock slope affecting a transportation corridor would certainly improve any assessment of the hazards caused by those movements. Various remote sensing technologies have the capability to be used to assess these processes; however, the optimal conditions under which the technology should be deployed are not clearly defined. Between December 2012 and December 2013 the efficacy of three remote sensing technologies (terrestrial and aerial LiDAR (light detection and ranging) and terrestrial photogrammetry) were compared for their ability to detect natural and anthropogenic changes at a location along the CN railway, in British Columbia, Canada. The results demonstrate a high degree of interoperability between the different technologies, the ability to map topographical change with all three technologies, and the limitations and (or) weaknesses of each technology with respect to mapping change. The project location and site accessibility represent a real world situation with nonideal facets, which challenge the capabilities of these state-of-the-art technologies. These results will aid decision-making with respect to implementation of remote sensing technologies to monitor changes to rock slopes adjacent to transportation corridors, which will lead to better understanding and assessment of hazards.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".