Applications of high-resolution space-borne SAR in mining disaster monitoring
Bibliographic record
Abstract
SAR(synthetic aperture radar) has more advantages of day/night capabilities,all weather capabilities,and wind/clouds/rain/snow/vegetation penetration capabilities and so on.It is the most important advance of space remote sensing and earth observation technology in recent 20 years.In the 21st century,a series of the high-resolution space-borne SAR successful operation symbolize the radar remote sensing and earth observation going into a new era.In this paper,the superiorities of the high-resolution SAR data and the application bottlenecks of the middle or low-resolution SAR data due to low-resolution are summarized.An overview of the current high-resolution sensors feature of SAR satellites in orbit,for example COSMO-Sky Med satellites of Italian,the Radarsat-2 satellite of Canada and the Terra SAR-X satellite of German,is described.The studies in the fields of geological disasters deformation monitoring by using high-resolution SAR backscatter information and phase information at home and abroad are referred,especially geological disasters monitoring in mining areas.Finally,a prospect of high-resolution SAR technology applications of mining subsidence and geological disasters monitoring is expected.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".