Incorporating Automatic Satellite Detections of Oil Spills with Numerical Fate and Trajectory Modeling
Bibliographic record
Abstract
ABSTRACT In recent years, access to freely available and commercial satellite imagery, such as Sentinel-1, RADARSAT-2, COSMO-SkyMed, and TerrsSAR-X, increased to the level where most global waters are observed at least once per day by one of these satellite platforms. The availability of this data combined with technological advancements in machine-learning and smart image segmentation allows for the potential to automatically detect oil spills and reduce the likelihood of false alarms. This improved satellite monitoring could result in early discovery of releases and the ability to launch a quicker response to mitigate potential damages. Numerical modeling will be used in combination with the detection results to determine the fate and trajectory of the oil as well as to hindcast where the oil was released. Implementing models into the process facilitates an effective response and incident investigation by determining where the oil is spreading and discovering where the oil originated. In 2019, Petroleum Research Newfoundland and Labrador (PRNL) launched a project led by C-CORE and RPS titled SpillSight to conduct a study into this technology for automatically detecting spills by satellite and modelling the outputs.
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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) | 0.001 | 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".