Ocean eddies play a critical role in marine ecosystems and global climate systems by influencing nutrient distribution, heat transport, and oceanic circulation patterns. Detecting and analyzing these dynamic structures is essential for understanding their impact on ocean processes and for applications such as climate modeling, marine resource management, and navigation. Traditional methods of detecting ocean eddies often rely on satellite altimetry data combined with algorithms that analyze sea surface height anomalies. While these techniques have been effective, they can be computationally intensive and may struggle with real-time detection or identifying smaller eddies. To address these challenges, this report presents an efficient and lightweight convolutional network designed specifically for ocean eddy detection, offering an innovative approach that balances accuracy and computational efficiency. The proposed convolutional network leverages the power of deep learning to process spatial and temporal oceanographic data, identifying eddies with high precision.
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