A Novel Approach in Oil Spill Detection, Identification, and Classification via Multisource Technologies and Artificial Intelligence - T. Avikasis Cohen; D. Angel; A. Brook (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)
The Mediterranean Sea has a substantial volume of maritime traffic, including many tankers ferrying oil from eastern sources to western refineries. This critical maritime front, vital for trade and connectivity, also poses a significant risk of oil spills due to these busy shipping routes. The conventional methods for early oil spill detection have encountered numerous challenges, primarily due to the complex and variable nature of spill events. This study promotes an anomaly-based approach, treating oil spills as environmental outliers, and utilizes baseline water parameter comparisons to detect and monitor sea oil spills effectively. This approach leverages satellite data, employing a combination of remote sensing techniques and advanced machine learning technologies. The end goal is providing a platform for monitoring and detecting oil spills, to empower users worldwide to conduct regular assessments, contributing to the proactive prevention of future environmental damage.