  Global climate change plays an essential role in our daily life. Mesoscale
ocean eddies have a significant impact on global warming, since they affect the
ocean dynamics, the energy as well as the mass transports of ocean circulation.
From satellite altimetry we can derive high-resolution, global maps containing
ocean signals with dominating coherent eddy structures. The aim of this study
is the development and evaluation of a deep-learning based approach for the
analysis of eddies. In detail, we develop an eddy identification and tracking
framework with two different approaches that are mainly based on feature
learning with convolutional neural networks. Furthermore, state-of-the-art
image processing tools and object tracking methods are used to support the eddy
tracking. In contrast to previous methods, our framework is able to learn a
representation of the data in which eddies can be detected and tracked in more
objective and robust way. We show the detection and tracking results on sea
level anomalies (SLA) data from the area of Australia and the East Australia
current, and compare our two eddy detection and tracking approaches to identify
the most robust and objective method.
