Automatic Detection for the Boundary of Earthquake Triggered Landslides with Sentinel-1 SAR Imagery
Bibliographic record
Abstract
Earthquake Triggered Landslides (ETLs) are serious large-scale secondary disasters of earthquakes, causing severe casualties and property losses, and therefore their rapid, automated and accurate detection is of great value. In this study, we proposed a novel deep learning-based ETL detection method to address this challenge. Firstly, the published ETL inventories, preand post-event SAR images, and optical remote sensing images are jointly used to generate high-quality landslide datasets for training in deep learning. Then, the datasets are input into a novel landslide detection network, which can effectively extract and fuse multi-level features of landslides to identify SAR pixels within the ETL. Finally, the ETL boundaries are determined based on the pixel-level ETL identification results. This method is verified through the landslide of earthquake case happened in Mainling (2017), China. Results show that our method can effectively identify landslide boundaries with high accuracy (over 80%), obviously outperforming other deep learning frameworks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".