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

EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies