Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges
Bibliographic record
Abstract
A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using DInSAR processing technique was developed at the Canada Center for Remote Sensing.Ground deformation rates from 2017 to 2023 were computed over a large territory of North America and Eurasia from more than 220,000 readily available Sentinel-1 images, and the performance and shortcomings of the developed processing system were analyzed.Here, we present the processing methodology and several examples of deformation rate maps and time series produced with this automated system.Examples include the deformation of slow-moving deep-seated landslides in two regions of Canada, subsidence at the Komsomolskoe oil field in the Russian Arctic, the Tengiz oil field in Kazakhstan, multiple large subsiding regions and landslides in northwestern Iran, and two large subsiding regions in the Yellow River Delta and Xinjiang, China.Many deformation processes observed in these deformation rate maps, including large landslides, have previously been unknown to the research community.Systematic radar penetration depth changes were observed in multiple regions and were investigate in detail for 1 Eurasian region.Computed deformation rates for North America and Eurasia are available to the research community and can be downloaded from the data repository. RSUMUn syst eme de traitement enti erement automatis e pour mesurer les s eries chronologiques de d eformation du sol a long terme et les taux de d eformation image par image a l'aide de la technique de traitement DInSAR a et e mis au point au Centre canadien de t el ed etection.Les taux de d eformation du sol de 2017 a 2023 ont et e calcul es sur un vaste territoire d'Am erique du Nord et d'Eurasie a partir de plus de 200 000 images Sentinel-1 facilement disponibles, et les performances et les lacunes du syst eme de traitement d evelopp e ont et e analys ees.Nous pr esentons ici la m ethodologie de traitement et plusieurs exemples de cartes de vitesse de d eformation et de s eries chronologiques produites avec ce syst eme automatis e. Les exemples incluent la d eformation de glissements de terrain profonds se d eplac ant lentement dans deux r egions du Canada, l'affaissement du champ p etrolif ere Komsomolskoe dans l'Arctique russe, le champ p etrolif ere Tengiz au Kazakhstan, plusieurs grandes r egions d'affaissements et de glissements de terrain dans le nord-ouest de l'Iran, ainsi que deux grandes r egions s'affaissant dans le delta du fleuve Jaune et le Xinjiang, en Chine.De nombreux processus de d eformation observ es dans ces cartes de vitesse de d eformation, y compris les grands glissements de terrain, etaient auparavant inconnus de la communaut e scientifique.Des changements syst ematiques de la profondeur de p en etration radar ont et e observ es dans plusieurs r egions et ont et e etudi es en d etail pour une r egion eurasienne.Les taux de d eformation calcul es pour l'Am erique du Nord et l'Eurasie sont a la
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".