MasTer: A Full Automatic Multi-Satellite InSAR Mass Processing Tool for Rapid Incremental 2D Ground Deformation Time Series
Bibliographic record
Abstract
The increasing amount of Synthetic Aperture Radar (SAR) satellites orbiting the Earth, their increasing time and space resolution, the variety of wavelengths, polarizations and looking geometries, the shortening of data availability latency and the lengthening of archive databases offer unprecedented opportunities for Earth observation and hazard monitoring. The downside is that it brings new challenges for processing that huge amount of data and for making the results quickly analyzable. To fully benefit from these advances in SAR, it requires efficient data processing infrastructure (optimized for processing speed, storage usage and security), efficient data visualization, and standardization of the final products for easy ingestion by conventional analysis tools. We present here the InSAR Mass processing Toolbox for Multidimensional time series (MasTer), which can combine any type of SAR data to produce unsupervised vertical and horizontal ground deformation time series. MasTer is optimized to automatically download SAR data, select the appropriate interferometric pairs, perform the interferometric mass processing, compute the geocoded deformation maps, invert and display the velocity maps and the 2D time series on a web page updated incrementally as soon as a new image is made available. The incremental architecture allows updating the time series within the shortest time possible (typically a few hours) as soon as a new SAR image is provided. Several steps are self-evaluating to ensure robust and reliable processing. Moreover, recent methodological improvement consists in the computation of a coherence proxy to guide the pair selection optimization balancing the use of each image as primary and secondary image during the differential interferometric (DInSAR) processing. Such a pair selection increases both the processing efficiency and the signal-to-noise ratio of the time series. MasTer also allows the production of time series of coherences or SAR amplitude images, which can be used e.g. for land use monitoring or geomorphological changes detection. The capabilities and performances of MasTer will be illustrated with several examples. Software and manual are available upon request from the authors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".