# SCSS-Net: Solar Corona Structures Segmentation by Deep Learning

arXiv: https://arxiv.org/abs/2109.10834 · HTML: https://mlsanity.com/paper/2109.10834/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Rethinking the Hyperparameters for Fine-tuning](https://mlsanity.com/paper/2002.11770/) (2020)
- [A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission](https://mlsanity.com/paper/1903.04538/) (2019)
- [Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data](https://mlsanity.com/paper/1906.11248/) (2019)

## Built on by (most cited first, 4 of 4)

- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [Surya: Foundation Model for Heliophysics](https://mlsanity.com/paper/2508.14112/) (2025)
- [AI Foundation Model for Heliophysics: Applications, Design, and Implementation](https://mlsanity.com/paper/2410.10841/) (2024)
- [Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk Hα Images](https://mlsanity.com/paper/2502.07259/) (2025)

## Abstract (arXiv)

Structures in the solar corona are the main drivers of space weather processes that might directly or indirectly affect the Earth. Thanks to the most recent space-based solar observatories, with capabilities to acquire high-resolution images continuously, the structures in the solar corona can be monitored over the years with a time resolution of minutes. For this purpose, we have developed a method for automatic segmentation of solar corona structures observed in EUV spectrum that is based on a deep learning approach utilizing Convolutional Neural Networks. The available input datasets have been examined together with our own dataset based on the manual annotation of the target structures. Indeed, the input dataset is the main limitation of the developed model's performance. Our textitSCSS-Net model provides results for coronal holes and active regions that could be compared with other generally used methods for automatic segmentation. Even more, it provides a universal procedure to identify structures in the solar corona with the help of the transfer learning technique. The outputs of the model can be then used for further statistical studies of connections between solar activity and the influence of space weather on Earth.