  Identification of solar coronal holes (CHs) provides information both for
operational space weather forecasting and long-term investigation of solar
activity. Source data for the first problem are typically most recent solar
disk observations, while for the second problem it is convenient to consider
solar synoptic maps. Motivated by the idea that the concept of CHs should be
similar for both cases we investigate universal models that can learn a CHs
segmentation in disk images and reproduce the same segmentation in synoptic
maps. We demonstrate that Convolutional Neural Networks (CNN) trained on daily
disk images provide an accurate CHs segmentation in synoptic maps and their
pole-centric projections. Using this approach we construct a catalog of
synoptic maps for the period of 2010-20 based on SDO/AIA observations in the
193 Angstrom wavelength. The obtained CHs synoptic maps are compared with
magnetic synoptic maps in the time-latitude and time-longitude diagrams. The
initial results demonstrate that while in some cases the CHs are associated
with magnetic flux transport events there are other mechanisms contributing to
the CHs formation and evolution. To stimulate further investigations the
catalog of synoptic maps is published in open access.
