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

TL;DR

This paper introduces a deep learning framework for large-scale detection and categorization of oil spills in SAR images, achieving state-of-the-art detection performance and providing valuable insights for oil spill management.

Contribution

It presents a novel neural network model for oil spill detection and classification in SAR images, along with an operational pipeline and visualization tool for global analysis.

Findings

State-of-the-art detection accuracy comparable to human operators

First application of classification for oil spill shape and texture

Operational pipeline enabling large-scale worldwide analysis

Abstract

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.