DInSAR products and applications for the RADARSAT Constellation Mission
Bibliographic record
Abstract
The Canadian RADARSAT Constellation Mission (RCM) is scheduled for launch in the second half of 2018, to support primarily maritime surveillance (sea ice, surface wind, oil pollution and ship monitoring), disaster management (mitigation, warning, response and recovery), and ecosystem monitoring (agriculture, wetlands, forestry and coastal change). The RCM consists of three C-band Synthetic Aperture Radar (SAR) satellites, equally spaced in a common orbit, providing rapid revisit capability. The RCM offers a selection of beam modes that vary in terms of spatial resolution, coverage, polarization and noise floor. The constellation orbit and beam mode options are sufficient to provide daily imaging opportunities of Canadian territories and Differential Interferometric Synthetic Aperture Radar (DInSAR) acquisitions with four days' revisit time. This new source of SAR data will strengthen operational applications that require a continuous stream of data. In Canada and around the world, DInSAR is regularly used for detection and monitoring of ground deformation (e.g. uplift, subsidence and horizontal motion). At Natural Resources Canada (NRCan), DInSAR derived information supports diverse activities such as detecting terrain instability in permafrost regions, monitoring glacier and ice cap dynamics, monitoring landslide risk sites, and tracking surface deformation related to bitumen extraction. In partnership with the Canadian Space Agency (CSA), the Canada Centre for Mapping and Earth Observation (CCMEO) at NRCan is developing an automated system for generating standard and advanced deformation products by means of DInSAR from data acquired by RADARSAT-2 and RCM satellites. This processing system consists of software and hardware components and is capable of providing non-expert users with on-demand change detection and deformation products computed from the SAR data. This system will allow scientists and resource managers to efficiently and effectively extract ground displacement information from the thousands of RCM acquisitions collected annually during its mission life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.039 | 0.028 |
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".