Mapping Sea Water Surface in Persian Gulf, Oil Spill Detection Using Sentinal-1 Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it