An Assessment of Earthquake-Induced Landslides Distribution in Nepal Using Open-Source Applications on Sentinel-1 Tops Sar Imagery
Bibliographic record
Abstract
The effects of landslide disasters are extremely severe, resulting in significant economic damage and a high number of fatalities on a global scale.In the event of a disaster of this magnitude, a swift and reliable disaster information is crucial.This is usually very tasking activities and expensive using proprietary application and data.This study therefore demonstrates the usability of opensource application and free satellite data on an assessment of earthquake induced landslide using data and applications sourced from European Space Agency (ESA) Copernicus Open Access Hub.A Differential Interferometric Synthetic Aperture Radar (DInSAR) technique, which is more advanced earthquake assessment tool was used to obtain morphological changes via-a-vis the vertical displacement produced during Gorkha earthquake of April 25, 2015, in Nepal.In the study, a single interferogram of the two repeat pass SAR data for a DInSAR process was applied over an area of 128 km 2 .A pair of SAR image with a temporal baseline of 144 days and perpendicular baseline of 122.51 meters were used.Three landslide locations were evaluated.The vertical displacements using DInSAR ranges from -0.23 (moving away from satellite) to 0.24 m (movement towards satellite).The simulated morphological values compared well with obtained google earth images captured at during the period of the disaster event.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".