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A large-scale solar dynamics observatory image dataset for computer vision applications
The National Aeronautics Space Agency (NASA) Solar Dynamics Observatory (SDO) mission has given us unprecedented insight into the Sun’s activity. By capturing approximately 70,000 images a day, this mission has created one of the richest and biggest repositories of solar image data available to mank...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5525637/ https://www.ncbi.nlm.nih.gov/pubmed/28765787 http://dx.doi.org/10.1038/sdata.2017.96 |
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author | Kucuk, Ahmet Banda, Juan M. Angryk, Rafal A. |
author_facet | Kucuk, Ahmet Banda, Juan M. Angryk, Rafal A. |
author_sort | Kucuk, Ahmet |
collection | PubMed |
description | The National Aeronautics Space Agency (NASA) Solar Dynamics Observatory (SDO) mission has given us unprecedented insight into the Sun’s activity. By capturing approximately 70,000 images a day, this mission has created one of the richest and biggest repositories of solar image data available to mankind. With such massive amounts of information, researchers have been able to produce great advances in detecting solar events. In this resource, we compile SDO solar data into a single repository in order to provide the computer vision community with a standardized and curated large-scale dataset of several hundred thousand solar events found on high resolution solar images. This publicly available resource, along with the generation source code, will accelerate computer vision research on NASA’s solar image data by reducing the amount of time spent performing data acquisition and curation from the multiple sources we have compiled. By improving the quality of the data with thorough curation, we anticipate a wider adoption and interest from the computer vision to the solar physics community. |
format | Online Article Text |
id | pubmed-5525637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-55256372017-08-01 A large-scale solar dynamics observatory image dataset for computer vision applications Kucuk, Ahmet Banda, Juan M. Angryk, Rafal A. Sci Data Data Descriptor The National Aeronautics Space Agency (NASA) Solar Dynamics Observatory (SDO) mission has given us unprecedented insight into the Sun’s activity. By capturing approximately 70,000 images a day, this mission has created one of the richest and biggest repositories of solar image data available to mankind. With such massive amounts of information, researchers have been able to produce great advances in detecting solar events. In this resource, we compile SDO solar data into a single repository in order to provide the computer vision community with a standardized and curated large-scale dataset of several hundred thousand solar events found on high resolution solar images. This publicly available resource, along with the generation source code, will accelerate computer vision research on NASA’s solar image data by reducing the amount of time spent performing data acquisition and curation from the multiple sources we have compiled. By improving the quality of the data with thorough curation, we anticipate a wider adoption and interest from the computer vision to the solar physics community. Nature Publishing Group 2017-07-25 /pmc/articles/PMC5525637/ /pubmed/28765787 http://dx.doi.org/10.1038/sdata.2017.96 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Kucuk, Ahmet Banda, Juan M. Angryk, Rafal A. A large-scale solar dynamics observatory image dataset for computer vision applications |
title | A large-scale solar dynamics observatory image dataset for computer vision applications |
title_full | A large-scale solar dynamics observatory image dataset for computer vision applications |
title_fullStr | A large-scale solar dynamics observatory image dataset for computer vision applications |
title_full_unstemmed | A large-scale solar dynamics observatory image dataset for computer vision applications |
title_short | A large-scale solar dynamics observatory image dataset for computer vision applications |
title_sort | large-scale solar dynamics observatory image dataset for computer vision applications |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5525637/ https://www.ncbi.nlm.nih.gov/pubmed/28765787 http://dx.doi.org/10.1038/sdata.2017.96 |
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