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

Redouane Lguensat 1, 2 Miao Sun 3 Ronan Fablet 1, 2 Evan Mason 4 Pierre Tandeo 2, 1 Ge Chen 3 
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
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
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Contributor : Pierre Tandeo Connect in order to contact the contributor
Submitted on : Monday, December 3, 2018 - 11:05:25 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 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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