  We propose a deep learning framework to detect and categorize oil spills in
synthetic aperture radar (SAR) images at a large scale. By means of a carefully
designed neural network model for image segmentation trained on an extensive
dataset, we are able to obtain state-of-the-art performance in oil spill
detection, achieving results that are comparable to results produced by human
operators. We also introduce a classification task, which is novel in the
context of oil spill detection in SAR. Specifically, after being detected, each
oil spill is also classified according to different categories pertaining to
its shape and texture characteristics. The classification results provide
valuable insights for improving the design of oil spill services by
world-leading providers. As the last contribution, we present our operational
pipeline and a visualization tool for large-scale data, which allows to detect
and analyze the historical presence of oil spills worldwide.
