{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/eddynet-a-deep-neural-network-for-pixel-wise","title":"EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies","arxiv_id":"1711.03954","date":"2017-11-10","proceeding":null,"authors":["Redouane Lguensat","Miao Sun","Ronan Fablet","Evan Mason","Pierre Tandeo","Ge Chen"],"abstract":"This work presents EddyNet, a deep learning based architecture for automated\neddy detection and classification from Sea Surface Height (SSH) maps provided\nby the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet is\na U-Net like network that consists of a convolutional encoder-decoder followed\nby a pixel-wise classification layer. The output is a map with the same size of\nthe input where pixels have the following labels \\{'0': Non eddy, '1':\nanticyclonic eddy, '2': cyclonic eddy\\}. We investigate the use of SELU\nactivation function instead of the classical ReLU+BN and we use an overlap\nbased loss function instead of the cross entropy loss. Keras Python code, the\ntraining datasets and EddyNet weights files are open-source and freely\navailable on https://github.com/redouanelg/EddyNet.","url_abs":"http://arxiv.org/abs/1711.03954v1","url_pdf":"http://arxiv.org/pdf/1711.03954v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"eddynet-a-deep-neural-network-for-pixel-wise","repo_url":"https://github.com/redouanelg/EddyNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"oceanic-eddy-classification","task_name":"Oceanic Eddy Classification"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}