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Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation
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  • Pascal Sado,
  • Lasse Boy Novock Clausen,
  • Wojciech Jacek Miloch,
  • Hannes Nickisch
Pascal Sado
University of Oslo, University of Oslo, University of Oslo

Corresponding Author:pascal.sado@fys.uio.no

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Lasse Boy Novock Clausen
University of Oslo, University of Oslo, University of Oslo
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Wojciech Jacek Miloch
University of Oslo, University of Oslo, University of Oslo
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Hannes Nickisch
Philips Research, Philips Research, Philips Research
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Abstract

We develop an open source algorithm to apply Transfer learning to Aurora image classification and Magnetic disturbance Evaluation (TAME). For this purpose, We evaluate the performance of 80 pretrained neural networks using the Oslo Auroral THEMIS (OATH) data set of all-sky images, both in terms of running time and feature accuracy. From the features extracted by the best network, we retrain the last neural network layer using the Support Vector Machine (SVM) algorithm to distinguish between the labels “arc”, “diffuse”, “discrete”, “cloud”, “moon” and “clear sky / no aurora”. This transfer learning approach yields 90% accuracy in the six classes; if we aggregate the 3 auroral and 3 non-aurora classes after classification, we achieve up to 98% accuracy. We apply our classifier to a new dataset of 550,000 images and evaluate the classifier based on these previously unseen images. To show the potential usefulness of our feature extractor and classifier, we investigate two test cases. First, we compare our predictions for the “cloudy” images to meteorological data and second we train a linear ridge model to predict perturbations in Earth’s locally measured magnetic field. We demonstrate that the classifier can be used as a filter to remove cloudy images from datasets and that the extracted features allow to predict magnetometer measurements. All procedures and algorithms used in this study are publicly available, and the code and classifier provided, which opens possibility for large scale studies of all-sky images.
Jan 2022Published in Journal of Geophysical Research: Space Physics volume 127 issue 1. 10.1029/2021JA029683