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During a project examining the use of machine learning techniquesfor oil spill detection, we encountered several essential questions that webelieve deserve the attention of the research community. We use ourparticular case study to illustrate such issues as problem formulation,selection of evaluation measures, and data preparation. We relate theseissues to properties of the oil spill application, such as its imbalancedclass distribution, that are shown to be common to many applications. Oursolutions to these issues are implemented in the Canadian EnvironmentalHazards Detection System (CEHDS), which is about toundergo field testing.

Machine Learning for the Detection of Oil Spills in Satellite Radar Images