RADARSAT-2 for mapping natural hazard events: case studies from around the world
Bibliographic record
Abstract
Natural hazard events such as earthquakes and volcanic eruptions can be successfully studied with the Synthetic Aperture Radar (S AR) that is capable of mapping sub-centimetre ground deformation over large areas using Interferometric SAR (InSAR) processing methodology. After the Japanese ALOS and European ENVISAT satellites completed their operation in 2 011 and 2 012 respectively , the only SAR sensors left in operation were the X- b and German TerraSAR-X, Italian Cosmo-SKYMED and C- band Canadian RADARSAT-2. In this poster we present a number of case studies were RADARSAT-2 has proven to be the sensor with the best archived coverage and characteristics, including excellent spatial and temporal resolution and a wavelength that is superior for land observations. Using RADARSAT-2, we successfully mapped a number of large earthquakes in Canada, Central America, Iran, Italy, and Russia, and volcanic deformation in Chile, Hawaii, Italy, and Spain. Here we will present deformation maps for some of the natural hazard events that were studied and provide suggestions for future missions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".