Canadian Geotechnical Colloquium: three-dimensional remote sensing, four-dimensional analysis and visualization in geotechnical engineering — state of the art and outlook
Bibliographic record
Abstract
Successful geotechnical projects occur when the design is based on a thorough understanding of the geologic and environmental systems and the interaction of these systems over time. The ability to examine and track movement through space and time has been an essential part of the geoprofessional’s toolkit since the onset of the practice. Since the early 2000s, high-resolution three-dimensional (3D) topographic data have begun to transform how we map and understand movement through time across spatially extensive regions at unprecedented levels of accuracy and confidence. This paper examines how high-resolution 3D topographical data, four-dimensional (4D) analysis, and visualization of data in 3D environments can improve our ability to better understand changes in the morphology and material behaviour through time, leading to better decisions and better outcomes. Evolution of advancements made over the past 20 years is presented through case studies where positive impacts were realized through the adoption of 3D remote sensing and 4D analysis, and cases where data could be used in the future to improve outcomes. The paper presents current research being done to further improve processing techniques and exploit new data collection and computational processing capabilities, pushing the capability of time-dependant 4D geotechnical monitoring to new limits.
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".