CNN Based Ocean Eddy Detection Using Cloud Services.

This study focuses on small-scale ocean eddy (<20km) detection in satellite remote images using Convolutional Neural Network (CNN) deployed on AWS cloud platforms, specifically SageMaker and EC2. Our goal is to streamline the workflow and make the services accessible in the climate change domain. In this work, we proposed a CNN-based Ocean Eddy detection model and it is deployed using Sage-Maker and EC2. Our proposed approach achieved more than 95% and 94% training and validation accuracy respectively by applying principal component analysis (PCA) on the dataset. We considered the usability, performance, and cost comparison while deploying the services. Our analysis shows that SageMaker and EC2 are highly capable of building CNN-based services, though there are some challenges in deploying services and limitations related to resources.