  We present a deep learning model with temporal memory to detect clouds in
image time series acquired by the Seviri imager mounted on the Meteosat Second
Generation (MSG) satellite. The model provides pixel-level cloud maps with
related confidence and propagates information in time via a recurrent neural
network structure. With a single model, we are able to outline clouds along all
year and during day and night with high accuracy.
