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

arXiv: https://arxiv.org/abs/1711.03954 · HTML: https://mlsanity.com/paper/1711.03954/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [Weight Initialization Techniques for Deep Learning Algorithms in Remote Sensing: Recent Trends and Future Perspectives](https://mlsanity.com/paper/2102.07004/) (2021)
- [Exploratory Lagrangian-Based Particle Tracing Using Deep Learning](https://mlsanity.com/paper/2110.08338/) (2021)
- [Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities](https://mlsanity.com/paper/2307.10803/) (2023)
- [Novel deep learning methods for 3D flow field segmentation and classification](https://mlsanity.com/paper/2305.11884/) (2023)
- [VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories](https://mlsanity.com/paper/2404.01352/) (2024)

## Abstract (arXiv)

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.