SCSS-Net: Solar Corona Structures Segmentation by Deep Learning

Supplemental materials to the paper published in MNRAS (arXiv, Full Text):

• Jupyter notebook SCSS-net_CH that contains analysis presented in the article related to the segmentation of coronal holes. The required utilities are in /src folder.
• Jupyter notebook SCSS-net_AR that contains analysis related to the segmentation of active regions
• Visualization of SCSS-net performance to segment coronal holes on the test set is presented in YouTube video
• Custom annotations of coronal holes and active regions

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How to cite:

Šimon Mackovjak, Martin Harman, Viera Maslej-Krešňáková, Peter Butka, SCSS-Net: solar corona structures segmentation by deep learning, Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 3, December 2021, Pages 3111–3124, https://doi.org/10.1093/mnras/stab2536

BibTex:
@article{10.1093/mnras/stab2536,
author = {Mackovjak, Šimon and Harman, Martin and Maslej-Krešňáková, Viera and Butka, Peter},
title = "{SCSS-Net: solar corona structures segmentation by deep learning}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {508},
number = {3},
pages = {3111-3124},
year = {2021},
month = {10},
issn = {0035-8711},
doi = {10.1093/mnras/stab2536},
url = {https://doi.org/10.1093/mnras/stab2536}
}