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Related Experiment Video

Updated: Sep 4, 2025

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging
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Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation.

P Sado1, L B N Clausen1, W J Miloch1

  • 1Department of Physics University of Oslo Oslo Norway.

Journal of Geophysical Research. Space Physics
|July 22, 2022
PubMed
Summary
This summary is machine-generated.

We developed an open-source algorithm for Transfer learning for Aurora image classification and Magnetic disturbance Evaluation (TAME). This method accurately classifies aurora images and aids in predicting magnetic field disturbances.

Keywords:
all sky imagerauroraauroral imagingmachine learning

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Area of Science:

  • Geophysics
  • Space Physics
  • Computer Science

Background:

  • Automated analysis of all-sky aurora images is crucial for space weather research.
  • Existing methods may lack efficiency and accuracy in classifying diverse aurora phenomena and related disturbances.

Purpose of the Study:

  • To develop and validate an open-source transfer learning algorithm (TAME) for aurora image classification and magnetic disturbance evaluation.
  • To assess the performance of various pretrained neural networks for aurora image feature extraction.
  • To enable large-scale analysis of all-sky aurora image datasets.

Main Methods:

  • Evaluated 80 pretrained neural networks on the Oslo Auroral THEMIS (OATH) dataset for runtime and feature predictive capability.
  • Retrained the final layer of the best-performing network using a Support Vector Machine (SVM) for six-class image classification (arc, diffuse, discrete, cloud, moon, clear sky/no aurora).
  • Applied the trained classifier to a new dataset of 550,000 images and tested its predictive capability for cloud cover and magnetic field perturbations.

Main Results:

  • Achieved 73% accuracy for six-class aurora image classification and up to 91% accuracy when aggregating into auroral and non-auroral classes.
  • Demonstrated the classifier's effectiveness in filtering cloudy images by comparing predictions with meteorological data.
  • Showed that extracted features can predict local magnetic field perturbations using a linear ridge model.

Conclusions:

  • The TAME algorithm provides an accurate and efficient method for aurora image analysis using transfer learning.
  • The open-source nature of the algorithm and classifier facilitates broader research and application in space physics.
  • This approach enhances the potential for large-scale studies of atmospheric and geomagnetic phenomena from all-sky imagery.