With the availability of data and computational technologies in the modern world, machine
learning (ML) has emerged as a preferred methodology for data analysis and prediction …

Based on their salient features we manually label 5,824 images from various Time History of
Events and Macroscale Interactions during Substorms (THEMIS) all‐sky imagers; the labels …

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 …

The activity of citizen scientists who capture images of aurora borealis using digital cameras
has recently been contributing to research regarding space physics by professional …

This research leverages data from the Day/Night Band (DNB) of the Visible Infrared Imaging
Radiometer (VIIRS) instrument onboard the Suomi National Polar‐orbiting Partnership (S …

An aurora is a unique geophysical phenomenon with polar characteristics that can be
directly observed with the naked eye. It is the most concentrated manifestation of solar …

Automatically classifying a huge amount of ground‐based auroral images is essential to
facilitate aurora morphology statistical research and aid in comprehending the …

The auroral arc is the typical track of the interaction between the solar wind and the Earth's
magnetosphere. A sketch of skeletons for arc‐like aurora is usually used to describe auroral …

Auroral classification plays a crucial role in polar research. However, current auroral
classification studies are predominantly based on images taken at a single wavelength …

Convolutional neural networks (CNNs) in deep learning enable the extraction of features in
image data. Through the multi-layer superposition of a convolutional neural network, we can …