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Oil spills are considered to be one of the biggest obstacles to marine and coastal environments. Effective surveillance, ship detection, and accurate oil spill detection are crucial for the relevant agencies to respond adequately and minimize environmental emissions and avoid further disruption. Satellites deployed for capturing the data have led to the ingestion of huge amounts of remote sensing data to systems but to analyze that data via human effort is a tedious and extensive task. Hence, modern literature suggests the use of machine learning in paradigms such as image segmentation, image recognition, object detection as a substitute for traditional techniques. This research applies the contemporary deep Learning methods over the dataset available from the European Space Agency (ESA). The paper proposes the use of the volumetric convolution net (V-Net) architecture in addition to image augmentation …