Satellite-derived digital surface models to improve geolocation of InSAR deformation measurements on bridges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Canadian bridges need structural health monitoring (SHM) to ensure their safety and longevity. In-situ inspections are expensive and time-consuming, and climate change effects on river bridges may make previous legacy inspection schedules inadequate. Remote sensing can help supplement in-situ inspections or alert bridge operators to the need for a new inspection. Satellite-based Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an emerging approach for bridge deformation monitoring that requires accurate geolocation of the PS targets for best results. If the as-built elevations on bridges are insufficiently accurate, geolocation errors may limit the usefulness of the PS-InSAR data for bridge SHM. Satellite stereo imagery can be used to derive Digital Surface Models (DSMs), which give the elevation of the top surfaces of structures. It is unknown whether DSMs derived from satellite imagery are adequate as height sources for ensuring accurate positioning of PS-InSAR targets when bridge drawings or surveys are unavailable or unreliable. A study evaluating PS-InSAR elevation corrections using a DSM from satellite imagery was conducted on the Samuel de Champlain Bridge in Montreal (QC), Canada. A tri-stereo image triplet was used to create a 1 m resolution DSM, which was evaluated for height accuracy against a historical survey and used to correct PS-InSAR data points for elevation. The PS-InSAR dataset was georeferenced with DSM height corrections and evaluated for accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it