  We develop a reliable, fully automatic method for the detection of coronal
holes, that provides consistent full-disk segmentation maps over the full solar
cycle and can perform in real-time. We use a convolutional neural network to
identify the boundaries of coronal holes from the seven EUV channels of the
Atmospheric Imaging Assembly (AIA) as well as from line-of-sight magnetograms
from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics
Observatory (SDO). For our primary model (Coronal Hole RecOgnition Neural
Network Over multi-Spectral-data; CHRONNOS) we use a progressively growing
network approach that allows for efficient training, provides detailed
segmentation maps and takes relations across the full solar-disk into account.
We provide a thorough evaluation for performance, reliability and consistency
by comparing the model results to an independent manually curated test set. Our
model shows good agreement to the manual labels with an intersection-over-union
(IoU) of 0.63. From the total of 261 coronal holes with an area
$>1.5\cdot10^{10}$ km$^2$ identified during the time range 11/2010 - 12/2016,
98.1% were correctly detected by our model. The evaluation over almost the full
solar cycle no. 24 shows that our model provides reliable coronal hole
detections, independent of the level of solar activity. From the direct
comparison over short time scales of days to weeks, we find that our model
exceeds human performance in terms of consistency and reliability. In addition,
we train our model to identify coronal holes from each channel separately and
show that the neural network provides the best performance with the combined
channel information, but that coronal hole segmentation maps can be also
obtained solely from line-of-sight magnetograms.
