Aurora-FM: Foundation Model for All-Sky Auroral Imagery

A self-supervised foundation model for understanding auroral dynamics through all-sky imagery

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🌌 Overview

Aurora-FM is a foundation model specifically designed for analyzing all-sky auroral imagery. Built on the SimCLR self-supervised learning framework, Aurora-FM learns rich representations of auroral phenomena without requiring extensive manual labeling, making it a powerful tool for space physics research and auroral morphology studies.

Key Features

• 🎯 Optimized for THEMIS Data: Trained extensively on THEMIS all-sky imager data
• 🔄 Transfer Learning Ready: Easily finetune for other all-sky imaging systems
• 🏆 State-of-the-Art Performance: Achieves SOTA results on auroral classification benchmarks
• 🔬 Morphology Detection: Identifies complex auroral structures including beads, omega bands, and more
• 📊 Self-Supervised: Trained using SimCLR, reducing dependence on labeled datasets

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🚀 Quick Start

TODO

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📊 Model Details

Architecture

Aurora-FM employs a ResNet-based encoder architecture trained with the SimCLR contrastive learning framework. This approach enables the model to learn meaningful representations of auroral phenomena through self-supervision.

Training Details:

• Primary Dataset: About 1m images taken during periods of high auroral activity from the THEMIS All-Sky Imager Network
• Training Framework: SimCLR (Simple Framework for Contrastive Learning of Visual Representations)
• Encoder Architecture: ResNet-18
• Input Resolution: 256×256 pixels
• Embedding Dimension: 512

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🔬 Training Methodology

Aurora-FM was trained using the SimCLR framework, which learns representations by maximizing agreement between differently augmented views of the same image. This approach is particularly well-suited for auroral imagery due to:

1. Natural Variations: Captures the inherent variability in auroral displays
2. Invariance to Reflections: Handles reflections of images
3. Scale Robustness: Handles different spatial scales of auroral structures
4. Limited Labels: Achieves strong performance without extensive manual annotation

Data Augmentation Pipeline

• Random flips over vertical and/or horizontal axes
• Random cropping and resizing
• Color jittering (preserving aurora characteristics)

📚 Citation

If you use Aurora-FM in your research, please cite:

@article{https://doi.org/10.1029/2024JH000292,
    author = {Johnson, Jeremiah W. and Öztürk, Doğacan Su and Hampton, Donald and Connor, Hyunju K. and Blandin, Matthew and Keesee, Amy},
    title = {Automatic Detection and Classification of Aurora in THEMIS All-Sky Images},
    journal = {Journal of Geophysical Research: Machine Learning and Computation},
    volume = {1},
    number = {4},
    pages = {e2024JH000292},
    keywords = {aurora, annotation, machine learning, THEMIS, all-sky images},
    doi = {https://doi.org/10.1029/2024JH000292},
    url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024JH000292},
    eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2024JH000292},
    note = {e2024JH000292 2024JH000292},
    year = {2024}
}

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📝 License

This project is licensed under the Creative Commons 4.0 License - see the LICENSE file for details.

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🙏 Acknowledgments

• THEMIS Mission: For providing the all-sky imager data
• SimCLR Framework: Chen et al. for the foundational self-supervised learning approach
• Space Physics Community: For valuable feedback

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Built with ❤️ for the space physics community