Oil Spill Detection

Oil spill detection research focuses on developing rapid and accurate methods to identify spills, minimizing environmental damage and improving response times. Current efforts utilize diverse data sources, including drone-captured RGB images, synthetic aperture radar (SAR) imagery, and hyperspectral remote sensing data, analyzed with machine learning techniques such as convolutional neural networks (CNNs), object detectors (like YOLOv8), and the Segment Anything Model (SAM). These advancements leverage both supervised and unsupervised learning approaches to improve detection accuracy and reduce reliance on extensive labeled datasets. The resulting technologies have significant implications for environmental monitoring, pollution control, and resource management.