YOLO based Ocean Eddy Localization with AWS SageMaker
Links to Files
Author/Creator
Author/Creator ORCID
Date
Type of Work
Department
Program
Citation of Original Publication
Al Mahmud Mostafa, Seraj, Jinbo Wang, Benjamin Holt, and Jianwu Wang. “YOLO Based Ocean Eddy Localization with AWS SageMaker.” 2024 IEEE International Conference on Big Data (BigData), December 2024, 3720–28. https://doi.org/10.1109/BigData62323.2024.10825286.
Rights
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Abstract
Ocean eddies play a significant role both at the sea surface and beneath it, contributing to the sustainability of marine ecosystems and influencing broader oceanic and climatic behaviors. Investigating ocean eddies is essential for monitoring changes in the Earth’s oceans and their impact on climate. This study focuses on benchmarking the performance of state-of-theart YOLO (You Only Look Once) models for locating small-scale (<20km) ocean eddies using satellite remote sensing images. We leverage AWS SageMaker for this evaluation, utilizing both single and multi-GPU configurations to explore the feasibility and efficiency of deploying AI applications in cloud-based environments. This research not only assesses the effectiveness of SageMaker in handling complex Earth science data but also provides insights into deployment challenges, resource management for large-scale data, and the overall user experience. The findings highlight the strengths and limitations of using SageMaker for remote sensing applications and suggest potential future research directions. Our code is open-sourced at https://shorturl.at/hcjmq.