Oil Spill Detection

This study explores the application of deep learning algorithms for the classification of oil spills in ocean containers. With the increasing incidence of oil spills and their catastrophic effects on marine ecosystems, there is a pressing need for rapid and accurate detection methods. We propose a convolutional neural network (CNN) model that can classify oil spill events from satellite imagery with high precision. The model is trained on a dataset comprising images of the ocean's surface, labeled as 'oil spill' or 'no oil spill.' The results demonstrate the model's effectiveness in distinguishing oil spills, which could be instrumental in enabling timely containment measures and mitigating environmental damage.

KEYWORDS: Convolutional Neural Network, Deep Learning, oil spill, Transfer Learning, resnet, Mobile net.