Replication Data for: Auroral Image Classification with Deep Neural Networks

Version 3.1
Replication Data for: Auroral Image Classification with Deep Neural Networks
Kvammen, Andreas; Wickstrøm, Kristoffer; McKay, Derek; Partamies, Noora, 2020, "Replication Data for: Auroral Image Classification with Deep Neural Networks", https://doi.org/10.18710/SSA38J, DataverseNO, V3
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Description
Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere magnetosphere environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statistics and for supporting scientists to study auroral images in an objective, organized and repeatable manner. Although previous studies have presented tools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with a high precision (>90%). This work considers seven auroral subclasses; breakup, colored, arcs-bands, discrete, patchy, edge and clear-faint. Five different deep neural network architectures have been tested along with the well known classification methods; k nearest neighbor (KNN) and support vector machine (SVM). A set of clean nighttime color auroral images, without ambiguous auroral forms, moonlight, twilight, clouds etc., were used for training and testing. The deep neural networks generally outperformed the KNN and SVM methods, and the ResNet-50 architecture achieved the highest performance with an average classification precision of 92%. Although the results indicate that high precision aurora classification is an attainable objective using deep neural networks, it is stressed that a common consensus of the auroral morphology and the criteria for each class needs.

The authors would like to thank Urban Brändström and the Swedish Institute of Space Physics for providing the original auroral image data. The image data archive is freely accessible at http://www2.irf.se/allsky/data.html, however, the users are obliged to contact the Kiruna Atmospheric and Geophysical Observatory before usage (2020-01-03)
Subject Computer and Information Science; Mathematical Sciences; Physics
Keyword auroral images, auroral classification, convolutional neural networks, aurora dataset, deep learning
Related Publication Kvammen, A., Wickstrøm, K., McKay, D., & Partamies, N. (2020). Auroral image classification with deep neural networks. Journal of Geophysical Research: Space Physics, 125, e2020JA027808. https://doi.org/10.1029/2020JA027808 doi: 10.1029/2020JA027808
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00_ReadMe.txt
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Published Apr 14, 2020
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ReadMe file where dataset and the files in repository is described.
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Auroral Image Classification with Deep Neural Networks.ipynb
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Published Jan 15, 2020
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MD5: ddd34b7f1d5d9754a71b45d70af39b93
Google colab notebook to training ResNet 18 classifier on aurora dataset.
data.npz
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Published Jan 15, 2020
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MD5: ee2755c2c09b8f2cf8395eae849a1484
File containing images and labels stored as numpy arrays.
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kagolicens.pdf
kagolicens.pdf
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Published Jan 15, 2020
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TestImages.zip
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Published Apr 14, 2020
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Zipped archive containing the test images of the dataset in .PNG format. The class of the image is included in the image title.
TrainingImages.zip
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Published Apr 14, 2020
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MD5: b27e63cd55a51e756d0c387a1b7be893
Zipped archive containing the training images of the dataset in .PNG format. The class of the image is included in the image title.
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The files in this dataset may be reused under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/), except for the file "data.npz", which is restricted as stated in the license agreement "kagolicens.pdf" attached to the dataset and attached below.

The image data archive is freely accessible at http://www2.irf.se/allsky/data.html, however, the users are obliged to contact the Kiruna Atmospheric and Geophysical Observatory before usage.


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License agreement for data obtained from Kiruna Atmospheric and Geophysical Observatory at the Swedish Institute of Space Physics

Kiruna Atmospheric and Geophysical Observatory (KAGO) is an organisational unit within Swedish Institute of Space Physics (IRF). Observing sites include: the Kiruna area, Jamton, Lycksele Ionospheric Observatory (LJO), Uppsala Ionospheric Observatory (UJO) and Tormestorp.

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