SCSS-Net: solar corona structures segmentation by deep learning

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