Proactive Landslide Risk Management Using Regional Lidar Change Detection
Bibliographic record
Abstract
The occurrence or reactivation and acceleration of landslides can occur unannounced and can result in significant impacts to life, property, or the environment. The processes of slope deformation and progressive failure are more active than many realize; rapid slope failures are often preceded by years of erosion, deformation, and smaller failures. In the last 10 years, the use of lidar-derived elevation models has supported the identification of landslides across large regions and is increasing the ability of geoprofessionals to identify precursory signs of failures. During the same time period, advanced computational techniques to numerically compare multiple bare-earth lidar point cloud datasets, known as lidar change detection (LCD), coupled with the development of automated workflows, have resulted in the ability to conduct LCD rapidly across large areas. This paper demonstrates how regional LCD can provide a more complete understanding of landslide hazards and better management of risk. The paper also presents preliminary tests of applying image segmentation techniques to support LCD analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".