Identification

Title

Automated classification of auroral images with deep neural networks

Abstract

Terrestrial auroras are highly structured that visualize the perturbations of energetic particles and electromagnetic fields in Earth's space environments. However, the identification of auroral morphologies is often subjective, which results in confusion in the community. Automated tools are highly valuable in the classification of auroral structures. Both CNNs (convolutional neural networks) and transformer models based on the self-attention mechanism in deep learning are capable of extracting features from images. In this study, we applied multiple algorithms in the classification of auroral structures and performed a comparison on their performances. Trans-former and ConvNeXt models were firstly used in the analysis of auroras in this study. The results show that the ConvNeXt model can have the highest accuracy of 98.5% among all of the applied algorithms. This study provides a direct comparison of deep learning tools on the application of classifying auroral structures and shows promising capability, clearly demonstrating that auto-mated tools can help to minimize the bias in future auroral studies.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7zs31gk

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2023-02-12T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact