  Clouds and aerosols provide unique insight into the chemical and physical
processes of gas-giant planets. Mapping and characterizing the spectral
features indicative of the cloud structure and composition enables an
understand-ing of a planet's energy budget, chemistry and atmospheric dynamics.
Current space missions to Solar System planets produce high-quality datasets,
yet the sheer amount of data obtained often prohibits detailed 'by hand'
analyses. Current techniques rely mainly on two approaches: identifying the
existence of spectral features by dividing the fluxes of two or more spectral
channels, and performing full radiative transfer calculations for individual
spectra. The first method is not sufficiently accurate and the second is not
easily scalable to the entire planetary surface. Here we have developed a deep
learning algorithm, PlanetNet, that is able to quickly and accurately map
spatial and spectral features across large, heterogeneous areas of a planet. We
use PlanetNet to delineate the major components of the 2008 storm on Saturn,
enhancing the scope of the area previously studied and indicating regions that
can be probed more deeply with radiative transfer models. Our
spectral-component maps indicate compositional and cloud variations of the vast
region affected by the storm, showing regions of vertical upwelling, and
diminished clouds at the centre of compact substorms
