Permanent Scatterers technology: a powerful state of the art tool for historic and future monitoring of landslides and other terrain instability phenomena
Bibliographic record
Abstract
This paper discusses the use of multiple image sets of SAR data acquired by past and current satellite platforms in monitoring landslides and other terrain instability issues. The basic mathematical model is presented, highlighting the critical parameters of interferometry in geological and geotechnical applications. The potential of the technology and its drawbacks related to availability of long temporal series of SAR acquisitions are also discussed. Extensive processing of many SAR scenes has demonstrated how multi-temporal data-sets can be successfully exploited for terrain monitoring, by identifying objects on the landscape that have a stable, point-like behaviour. These objects, referred to as Permanent Scatterers (PS), can be geo-coded and monitored for movement very accurately, acting as a “natural” geodetic network. The paper presents examples of applications of monitoring landslides, settlement and subsidence, using experience in Italy and Canada, and concludes with a discussion on future directions for InSAR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".