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Through its magnetic activity, the Sun governs the conditions in Earth'svicinity, creating space weather events, which have drastic effects on ourspace- and ground-based technology. One of the most important solar magneticfeatures creating the space weather is the solar wind, that originates from thecoronal holes (CHs). The identification of the CHs on the Sun as one of thesource regions of the solar wind is therefore crucial to achieve predictivecapabilities. In this study, we used an unsupervised machine learning method,$k$-means, to pixel-wise cluster the passband images of the Sun taken by theAtmospheric Imaging Assembly on {\it the Solar Dynamics Observatory} (AIA/SDO)in 171 \AA, 193 \AA\,, and 211 \AA\,in different combinations. Our results showthat the pixel-wise $k$-means clustering together with systematic pre- andpost-processing steps provides compatible results with those from complexmethods, such as CNNs. More importantly, our study shows that there is a needfor a CH database that a consensus about the CH boundaries are reached byobservers independently. This database then can be used as the "ground truth",when using a supervised method or just to evaluate the goodness of the models.

Identification of Coronal Holes on AIA/SDO images using unsupervised Machine Learning