Automated Oil Spill Detection System

The integration of artificial intelligence, satellite remote sensing, and vessel tracking technologies has revolutionized oil spill detection, enabling real-time monitoring and rapid response. However, traditional detection methods based on manual surveillance [1], leading to delays and increased environmental damage. This research presents an automated oil spill detection system that leverages a multi-source approach, combining Automatic Identification System (AIS) data with Synthetic Aperture Radar (SAR) satellite imagery. The system employs machine learning algorithms to analyze vessel behavior, detect anomalies, and verify spills using high-resolution SAR images. The model is trained on diverse datasets to improve accuracy [2] [3] under varying weather and lighting conditions. Experimental results demonstrate the system’s ability to significantly reduce detection time while maintaining high accuracy, proving the effectiveness of this approach in minimizing ecological and economic impacts