Mapping Sea Water Surface in Persian Gulf, Oil Spill Detection Using Sentinal-1 Images
Bibliographic record
Abstract
The present study investigates an oil spill event in the Al Khafji region using Sentinel 1 SAR images.Al Khafji is on the border between Saudi Arabia and Kuwait in the Persian Gulf and it is considered a neutral zone.Al Khafji region has the potential to produce more than 470,000 barrels of oil per day (Mbbl/d).Methods based on multi sensor satellite images (Sentinel-2, Landsat 8, Terra, Cosmo_SkyMed, RADARSAT, etc.) analysis have been developed for detecting oil slicks from known natural seeps as well as oil spill events.In this paper, one of these methods is applied to Sentinel 1 images of a known area of natural oil outflow and of a recent oil spill event in Al Khafji zone.The Synthetic Aperture Radar (SAR) is recognized as the most important remote sensing tool for sea and ocean waters oil spill monitoring, recording, documentation and dissemination.Oil spills have been detected and characterized by using the SAR images over the Persian Gulf.In particular, this paper discusses oil spills detection in the Persian Gulf assessed by using Sentinel 1 (SAR) images.Results showed the suitability of the VV polarization of the Sentinel-1 for detecting oil-spills as well as the reduced utility of the VH polarization in this context.
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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 | 0.000 |
| 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 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".