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

arXiv: https://arxiv.org/abs/2006.13575 · HTML: https://mlsanity.com/paper/2006.13575/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms](https://mlsanity.com/paper/2105.14159/) (2021)
- [Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection](https://mlsanity.com/paper/2201.03016/) (2022)
- [Recognition of polar lows in Sentinel-1 SAR images with deep learning](https://mlsanity.com/paper/2203.16401/) (2022)
- [Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation](https://mlsanity.com/paper/2412.08116/) (2024)
- [Promptable Foundation Models for SAR Remote Sensing: Adapting the Segment Anything Model for Snow Avalanche Segmentation](https://mlsanity.com/paper/2601.01213/) (2026)
- [Deep learning based automatic detection of offshore oil slicks using SAR data and contextual information](https://mlsanity.com/paper/2204.06371/) (2022)
- [Monitoring snow avalanches from SAR data with deep learning](https://mlsanity.com/paper/2502.18157/) (2025)
- [Enhancing Cross Domain SAR Oil Spill Segmentation via Morphological Region Perturbation and Synthetic Label-to-SAR Generation](https://mlsanity.com/paper/2512.02290/) (2025)

## Abstract (arXiv)

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.