Estimating volume of large slow-moving deep-seated landslides in northern Canada from DInSAR-derived 2D and constrained 3D deformation rates
Bibliographic record
Abstract
Large slow-moving deep-seated landslides are observed in two different regions of northern Canada with advanced Differential Synthetic Aperture Radar (DInSAR). Two-dimensional vertical and horizontal east-west deformation rates and time series are computed from ascending and descending Sentinel-1 imagery acquired during 2017–2022. The landslides' east-west deformation rate is significantly larger than the vertical deformation rate, so it is better suited for landslide characterization. The deformation rates remain nearly constant and unaffected by seasonal changes during the entire period, suggesting substantial landslide thickness. Two large landslides in Alberta and the largest landslide in the Northwest Territories are studied in detail to demonstrate various advanced value-added products produced from DInSAR results. From ascending and descending line-of-sight deformation rates, Surface-Parallel Flow (SPF) and Aspect-Parallel Flow (APF)-constrained three-dimensional (3D) deformation rates are computed. Landslides thicknesses are then estimated from the APF-constrained 3D deformation rates, and the limitations of these techniques are discussed. The estimated thickness of the Northwest Territories landslide reaches 100 m, suggesting that the entire permafrost block may be sliding above the non-permafrost ground. The described techniques allow for mapping slow-moving deep-seated landslides in harsh conditions in areas affected by seasonal land cover changes, as in northern Canada. The decomposition of landslide motion into two or three components in certain conditions allows us to derive landslide thickness and volume and improve the estimation of a potential hazard posed by landslides.
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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".