  Finding potential life harboring exo-Earths is one of the aims of
exoplanetary science. Detecting signatures of life in exoplanets will likely
first be accomplished by determining the bulk composition of the planetary
atmosphere via reflected/transmitted spectroscopy. However, a complete
understanding of the habitability conditions will surely require mapping the
presence of liquid water, continents and/or clouds. Spin-orbit tomography is a
technique that allows us to obtain maps of the surface of exoplanets around
other stars using the light scattered by the planetary surface. We leverage the
potential of deep learning and propose a mapping technique for exo-Earths in
which the regularization is learned from mock surfaces. The solution of the
inverse mapping problem is posed as a deep neural network that can be trained
end-to-end with suitable training data. We propose in this work to use methods
based on the procedural generation of planets, inspired by what we found on
Earth. We also consider mapping the recovery of surfaces and the presence of
persistent cloud in cloudy planets. We show that the a reliable mapping can be
carried out with our approach, producing very compact continents, even when
using single passband observations. More importantly, if exoplanets are
partially cloudy like the Earth is, we show that one can potentially map the
distribution of persistent clouds that always occur on the same position on the
surface (associated to orography and sea surface temperatures) together with
non-persistent clouds that move across the surface. This will become the first
test one can perform on an exoplanet for the detection of an active climate
system. For small rocky planets in the habitable zone of their stars, this
weather system will be driven by water, and the detection can be considered as
a strong proxy for truly habitable conditions.
