  The potential of using remote sensing imagery for environmental modelling and
for providing real time support to humanitarian operations such as hurricane
relief efforts is well established. These applications are substantially
affected by missing data due to non-structural noise such as clouds, shadows
and other atmospheric effects. In this work we probe the potential of applying
a cycle-consistent latent variable deep generative model (DGM) for denoising
cloudy Sentinel-2 observations conditioned on the information in cloud
penetrating bands. We adapt the recently proposed Fr\'{e}chet Distance metric
to remote sensing images for evaluating performance of the generator,
demonstrate the potential of DGMs for conditional denoising, and discuss future
directions as well as the limitations of DGMs in Earth science and humanitarian
applications.
