Field trials for a rapid deployment lidarbased high temporal-frequency change detection monitoring system
Bibliographic record
Abstract
Remote sensing technologies are now prevalent in geohazard monitoring applications. Laser scanners (LiDAR), photogrammetry, and satellite-based topographical measurements can be used to map various geomorphologies, natural terrains, and man-made infrastructures. Change detection analysis with topographical data sets captured at a certain frequency can denote failure precursors, and active landslide events, and analyze events a-posteriori. Change detection in time is often conducted at high resolution and low temporal frequency. Dense data sets considerably reduce the inherent uncertainty arising from superficial roughness and statistical spread between two series of surveys. Long-term, high-resolution monitoring thus allows for precise measurements and the detection of relatively low change detection thresholds. Such an application is not readily applicable to the detection of rapidly progressing events. When statistical outliers occur such as intense rains, or earthquakes, landslides may be triggered and not captured within the time frame required to act and mitigate. The present study investigates low-resolution, high-temporal frequency measurements as an option for capturing rapidly evolving events. In this work, a low-cost fixed laser scanner was connected to a small Raspberry Pi to collect point cloud frames at hourly intervals. The montage consisted of the lidar-computer combination mounted on a tripod anchored to the ground. This paper describes the montage, material required to assemble, and installation protocol. Special considerations were given for rapid deployment with limited labor. To evaluate the monitoring setup, two experiments were done. One was that an artificial pile of humid less than 4 mm material was assembled in a local quarry. The pile was partially excavated at the toe to achieve a progressive failure as the material dried up. Monitoring of the pile was performed for approximately 3 weeks with hourly readings. A high-resolution scan was taken at the onset of the test for reference. The second experiment consisted of analyzing a floodplain in Grizzly Creek, Yukon for 10 months during the winter season, with six readings a day. Snow developed across the months and melted near the end of the period. A high-resolution scan was taken at the onset of both tests for reference. The results obtained are analyzed and discussed in the thesis. A discussion is provided on uncertainty associated with topographical measurements collected during this experiment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".