Land Subsidence Monitoring in Greater Vancouver Through Synergy of InSAR and Polarimetric Analysis
Bibliographic record
Abstract
Up-to-date spatial information on ground movements and land use is useful for emergency management of coastal regions. Time series InSAR techniques have proven to be effective tools for providing the former; however, InSAR results alone cannot be used to characterize the relationship between movements and land use. The focus of this study is to evaluate the potential to use high resolution radar satellite imagery for monitoring urban land subsidence associated with the construction of new building in Canadian coastal cities. To do this, we propose to integrate InSAR and polarimetric SAR information for deformation analysis. The methodology included multidimensional small baseline subset (MSBAS) InSAR analysis, polarimetric SAR change detection, and integration of the coherence, deformation, and polarimetric information for identifying the urban surface movements related to new buildings. The study was conducted in the Vancouver region, BC using RADARSAT-2 satellite data of ultra-fine mode and fine-quad mode acquired during 2010-2016. Results demonstrated that the integration of polarimetric and InSAR data permitted identification of ground movement, and the association of these movements to the new constructions in the urban environment. Several locations have been experiencing subsidence at a rate of up to 10 cm/year, and horizontal motion of 5 cm/year.
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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 | 0.000 |
| 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 | 0.000 |
| 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".