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We propose a deep learning framework to detect and categorize oil spills insynthetic aperture radar (SAR) images at a large scale. By means of a carefullydesigned neural network model for image segmentation trained on an extensivedataset, we are able to obtain state-of-the-art performance in oil spilldetection, achieving results that are comparable to results produced by humanoperators. We also introduce a classification task, which is novel in thecontext of oil spill detection in SAR. Specifically, after being detected, eachoil spill is also classified according to different categories pertaining toits shape and texture characteristics. The classification results providevaluable insights for improving the design of oil spill services byworld-leading providers. As the last contribution, we present our operationalpipeline and a visualization tool for large-scale data, which allows to detectand analyze the historical presence of oil spills worldwide.

Large-scale detection and categorization of oil spills from SAR images with deep learning