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.

Atmospheric gravity waves are produced when gravity attempts to restore disturbances through stable layers in the atmosphere. They have a visible effect on many atmospheric phenomena such as global circulation and air turbulence. Despite their importance, however, little research has been conducted on how to detect gravity waves using machine learning algorithms. We faced two major challenges in our research: our raw data had a lot of noise and the labeled dataset was extremely small. In this study, we explored various methods of preprocessing and transfer learning in order to address those challenges. We pre-trained an autoencoder on unlabeled data before training it to classify labeled data. We also created a custom CNN by combining certain pre-trained layers from the InceptionV3 Model trained on ImageNet with custom layers and a custom learning rate scheduler. Experiments show that our best model outperformed the best performing baseline model by 6.36% in terms of test accuracy.