Staring Spotlight TerraSAR-X SAR Interferometryfor Identification and Monitoring of Small-ScaleLandslide Deformation
Bibliographic record
Abstract
We discuss enhanced processing methods for high resolution Synthetic Aperture Radar(SAR) interferometry (InSAR) to monitor small landslides with difficult spatial characteristics,such as very steep and rugged terrain, strong spatially heterogeneous surface motion,and coherence-compromising factors, including vegetation and seasonal snow cover. The enhancedmethods mitigate phase bias induced by atmospheric effects, as well as topographic phase errorsin coherent regions of layover, and due to inaccurate blending of high resolution discontinuouswith lower resolution background Digital Surface Models (DSM). We demonstrate the proposedmethods using TerraSAR-X (TSX) Staring Spotlight InSAR data for three test sites reflecting diversechallenging landslide-prone mountain terrains in British Columbia, Canada. Comparisons withcorresponding standard processing methods show significant improvements with resultingdisplacement residuals that reveal additional movement hotspots and unprecedented spatial detailfor active landslides/rockfalls at the investigated sites.
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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 | 0.000 | 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".