Bibliographic record
Abstract
This paper shows that InSAR perspective view and airborne InSAR images are very useful for geomorphic characterization of landslide features in high relief terrains. Landslides cause approximately 1000 deaths a year, worldwide, with property damage of about US $4 billion. Developing new remote sensing techniques to identify and characterise landslides and debris flows will assist in the current national landslide inventory and hazard mapping in mountainous areas. Geomorphic characterization using large scale air photos are essential for large scale landslide hazard zonation maps. This is effectively done from the interpretation of large scale stereo air photographs and field mapping. In this study, we report on the use high-resolution airborne InSAR and perspective visualization techniques to map detailed landslide features in high relief terrains. We also show that high resolution fine mode (8m) RADARSAT image (40-50 degrees), although not as useful as the airborne InSAR images, can be used to identify some landslide features, thereby assisting in hazard mapping. The SAR image techniques provided information on detail slope profiles of the large rockslides occurring on steep slopes and along faults. From the images, faults, rock slumps, block slides, slide scars and debris slopes and ridges were identified. This study points the way of the potential of using high resolution optical and SAR stereo images to identify landslide features in areas where air photos are not readily available.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".