Morphological Change Detection at Pipeline Crossings Using Remote Sensing: A Proof-of-Concept Study
Bibliographic record
Abstract
Abstract A proof-of-concept research and development study was undertaken to determine to what degree changes in river morphology could be detected from satellite imagery in support of pipeline geohazard monitoring programs. Algorithms for the detection of the wetted extent and river centerline were developed in the Python programming language and applied to synthetic aperture radar and multispectral imaging data. Additional algorithms were developed to highlight meaningful changes in wetted extent and river centerline occurring between subsequent data acquisitions. The results showed that it was possible to detect morphological change using satellite imagery, but that the process was effective for rivers with a higher signal to noise ratio represented by the ratio of river width to image pixel size. For smaller rivers (20–50 m), the change in the centerline location was a more useful metric of morphological change. The change detection algorithms were successful at highlighting possibly important changes between subsequent data acquisitions but were limited by the relatively short catalogue of available images to compare changes against. Future work involves the improvement and deployment of the algorithms in operational software systems to support pipeline geohazard management programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".