Predicting the Future by Mapping the Past: Revolutionary Innovations in Lidar Change Detection Analysis are Enabling Regional Scale Mapping and Identification of Threats From Geohazards
Bibliographic record
Abstract
Abstract Managing pipeline integrity with respect to natural threats requires geoprofessionals to consider how the earth may behave into the future. Predicting morphological change involves a deep understanding of geology, geological processes, climate change, and knowledge of physical changes that have happened in the past or may occur in the future. One of the most capable techniques for mapping changing terrain through time across spatially extensive regions is lidar change detection (LCD) with airborne lidar scanning (ALS) data. LCD has typically been performed on a site-specific basis at known geohazard locations. However, with recent developments in data acquisition and processing, LCD is now a cost-effective tool that can be used on a systemwide scale to aid in the identification of potential geologic hazards and monitor known geohazard sites for active ground displacements that may be impactful to pipeline infrastructure. It enables monitoring of hazards directly impacting the right of way, as well as peripheral hazards that could encroach on the right of way. This proactive method of identifying and monitoring geohazards significantly enhances the ability of pipeline operators to make informed decisions and design resilient infrastructure. The work presented demonstrates how over 40,000 linear kilometers of LCD analysis was executed and integrated with a geohazard management program to support proactive decision-making across the eastern US.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".