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

Abstract : 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 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}. Keras Python code, the training datasets and EddyNet weights files are open-source and freely available on https://github.com/redouanelg/EddyNet.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-01929509
Contributor : Pierre Tandeo <>
Submitted on : Monday, December 3, 2018 - 11:05:25 AM
Last modification on : Wednesday, June 24, 2020 - 4:19:47 PM

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Redouane Lguensat, Miao Sun, Ronan Fablet, Evan Mason, Pierre Tandeo, et al.. EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies. International Geoscience and Remote Sensing Symposium (IGARSS 2018), Jul 2018, Valence, Spain. pp.1764-1767, ⟨10.1109/IGARSS.2018.8518411⟩. ⟨hal-01929509⟩

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