High-performance system for monitoring ground deformation from RCM SAR data
Bibliographic record
Abstract
Differential Interferometric Synthetic Aperture Radar (DInSAR) based mapping of surface deformation has proven valuable in a variety of geoscience applications. Conventional approaches to DInSAR analysis require significant expertise and are not suited to addressing the opportunities and challenges presented by the large multi-temporal SAR datasets generated by future radar constellations. As a result, the Canada Centre for Mapping and Earth Observation (CCMEO) developed, in support of Natural Resources Canada and Government of Canada priorities, a system for automatic generation of standard and advanced deformation products based on DInSAR technology from RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) data. Existing RADARSAT-2 processing algorithms were adapted to RCM specifications and novel advanced processing algorithms were developed to address the large data sets resulting from the constellation’s four-day rapid revisit cycle. This permitted expanding the DInSAR functionality across a wide-range of spatial and temporal scales. The system architecture is scalable and can be expanded to serve a large number of clients; it can simultaneously address multiple application areas including natural and anthropogenic hazards, natural resource development, permafrost and glacier monitoring, coastal and environmental change and wetlands mapping.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".