Assessing the potential of interferometric SAR for monitoring linear transportation infrastructure: case studies from Eastern Ontario
Bibliographic record
Abstract
Two RADARSAT-2 Spotlight (SLA24 and SLA74), and one Sentinel-1A (IW) SAR datasets are used to assess the potential of InSAR for monitoring linear transportation infrastructure subject to geohazards. A variety of case studies in Cornwall, Ontario are examined. An InSAR processing sequence was established for the RADARSAT-2 datasets; 19 SLA24 and 15 SLA74 images were used to create time-series deformation maps of Cornwall spanning March 2015 to September 2016. The noise floors were ± 1.5 cm and ± 1.0 cm, respectively. Phase unwrapping errors, atmospheric path delay, and limited SAR data were identified as the largest contributors to noise. The InSAR processing sequence was adapted to the Sentinel-1A dataset, and used to create unwrapped differential interferograms. A discussion of the phase components of these interferograms is presented. Large SAR datasets, small incidence angles, moderate image resolution, and 6-12 day revisit periods are recommended for monitoring linear transportation infrastructure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".