Development of an algorithm for automatic detection of oil slicks from synthetic aperture radar (SAR) imagery in the Gulf OF Guinea
Bibliographic record
Abstract
Pollution in the marine environment caused by oil spills is of great concern to coastal states due to its ecological, environmental and socio-economic impacts. The main objective of this research was to develop an adaptive oil spill detection algorithm for the Gulf of Guinea, and to estimate the location and spatial extent of oil slick in an acquired SAR imagery. The relevance of the use of space borne data for oil slick monitoring is evident in increased vessel traffic and oil drilling activities off the coast of West Africa. Image processing of acquired SAR image of the region involved the application of a median filter, local thresholding, classification, area calculation, and location extraction. Two dark spots were classified as slicks on Radarsat-2 imagery acquired on 18 May, 2008. The information derived from this research is essential for automatic processing and future implementation of oil slick detection and monitoring programme in the Gulf of Guinea.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".