Satellite synthetic aperture radar in the prosecution of illegal oil discharges
Bibliographic record
Abstract
Illegal oil discharges from ships are a problem that affects the world's oceans. Aircraft has been the main surveillance method since the 1960s; however, the advent of earth observation satellites offers many advantages over this traditional technique. In the past, oblique aerial photographs and optical satellite imagery have been used as evidence to prosecute illegal discharges; but satellite Synthetic Aperture Radar (SAR) imagery has not been used as frequently. During this thesis research, the legal challenges of using remote sensing as evidence in the prosecution of illegal oil discharges were investigated. A review of the legal literature revealed two limitations on the use of remote sensing within a legal context, which included the admissibility and authentication of evidence. The admissibility and authentication of satellite SAR imagery and oblique photographs as evidence in the prosecution of illegal oil discharges were the focus of this research. Expert witness qualifications and the reliability of the two methods were outlined to address admissibility. All of the elements of the image interpretation used in the identification of oil slicks using oblique aerial photographs and SAR imagery were compiled to address the legal requirement of authentication. In addition, standards were shown to be used within each remote sensing method. A case study using a RADARSAT-1 SAR image and oblique aerial photographs from an oil pollution incident off the coast of Newfoundland, Canada, was used to illustrate the legal chain of custody and how these data can be presented as evidence. The results from this analysis revealed that there are no technological barriers to satellite SAR images as evidence in court for illegal ship discharges when used in conjunction with oblique aerial photographs.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".