Oil Spill

Oil spill detection from Synthetic Aperture Radar (SAR) imagery is crucial for effective environmental cleanup and mitigation efforts. Current research heavily focuses on developing advanced deep learning models, such as generative adversarial networks and those incorporating the Segment Anything Model, to improve the accuracy and efficiency of oil spill segmentation from SAR images, often addressing the challenge of limited training data. These models leverage the unique characteristics of SAR backscatter to better identify and delineate oil slicks, improving the precision of segmentation maps. This improved accuracy translates to more effective response strategies and better assessment of environmental damage.