  This work presents EddyNet, a deep learning based architecture for automated
eddy detection and classification from Sea Surface Height (SSH) maps provided
by the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet is
a U-Net like network that consists of a convolutional encoder-decoder followed
by a pixel-wise classification layer. The output is a map with the same size of
the input where pixels have the following labels \{'0': Non eddy, '1':
anticyclonic eddy, '2': cyclonic eddy\}. We investigate the use of SELU
activation function instead of the classical ReLU+BN and we use an overlap
based loss function instead of the cross entropy loss. Keras Python code, the
training datasets and EddyNet weights files are open-source and freely
available on https://github.com/redouanelg/EddyNet.
