Automated oil spill detection using deep learning and SAR satellite data for the northern entrance of the Suez Canal.
Journal:
Scientific reports
Published Date:
Jun 20, 2025
Abstract
Oil spills threaten marine ecosystems, demanding swift detection and response. The northern entrance of the Suez Canal, a critical maritime route, is increasingly at risk of frequent oil spill incidents. This study employs the DeepLabv3 + deep learning model to automatically detect oil spills in the study area based on Sentinel-1 Synthetic Aperture Radar imagery provided by the European Space Agency. The model was trained separately on two datasets: the European Maritime Safety Agency CleanSeaNet (EMSA-CSN) dataset, comprising 1100 oil spill incidents, and a localized dataset containing 1500 oil spill incidents that occurred at the Egyptian territorial waters. A comparative analysis between the two models was conducted using 30 oil spill test cases located within the study area. The model trained on Egyptian data outperformed the EMSA-CSN-data- trained model, achieving a loss of 0.0516, an accuracy of 98.14%, a mean Intersection over Union (MIoU) of 0.7872, and a significantly higher ROC area of 0.91, compared to a loss of 0.1152, an accuracy of 96.45%, a MIoU of 0.7161, and a ROC area of 0.76 for the EMSA-CSN model. In addition, the area prediction analysis confirmed the superior performance of the Egyptian-data-trained model, which estimated a total affected area of 421.20 km, closely aligning with the ground truth of 425.20 km, whereas the EMSA-CSN-data-trained model underestimated oil spills of around 323.98 km. These results highlight the benefits of region-specific training in improving segmentation quality and reducing errors. This study emphasizes the potential of AI-driven models for real-time oil spill monitoring, with applications in environmental protection and emergency response.
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