A Deep Learning Approach for Automatic Ionogram Parameters Recognition With Convolutional Neural Networks
Approved
Classifications
MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal
Publication channel information
Title of journal/series
Earth and space science
ISSN (electronic)
2333-5084
ISSN (linking)
2333-5084
Publisher
American geophysical union
Publication forum ID
87942
Publication forum level
1
Internationality
Yes
Detailed publication information
Publication year
2024
Bibliographical publication year
2024
Reporting year
2024
Journal/series volume number
11
Journal/series issue number
10
Article number
e2023EA003446
DOI
10.1029/2023EA003446
Language of publication
English
Co-publication information
International co-publication
No
Co-publication with a company
No
Availability
Link to online publication
Link to self-archived version
Classification and additional information
MinEdu field of science classification
115 Astronomy, Space science
Keywords
deep learning; image recognition; ionogram parameters scaling; convolution neural networks; ionogram
Funding information
Funding information in the publication
The Ionospheric Situational Awareness (ISAw) project funded by the University of Oulu's Kvantum Institute.
Research data information
Research data information in the publication
The ionograms, ground truth data and CNN models utilized in the study are available at Zenodo via https://doi.org/10.5281/zenodo.13643235. Software to use deep learning models and evaluate results of article is preserved at https://github.com/RuslanSherstyukov/Ionogram-recognition.git. The ionospheric parameters automatically scaled by CNN models are available at https://www.sgo.fi/Data/Ionosonde/latestIonosonde.php.
Identifiers
Dataset identifier
10.5281/zenodo.13643235
Source database ID
WoS ID
WOS:001320204000001
Scopus ID
2-s2.0-85205039420