  Satellite images hold great promise for continuous environmental monitoring
and earth observation. Occlusions cast by clouds, however, can severely limit
coverage, making ground information extraction more difficult. Existing
pipelines typically perform cloud removal with simple temporal composites and
hand-crafted filters. In contrast, we cast the problem of cloud removal as a
conditional image synthesis challenge, and we propose a trainable
spatiotemporal generator network (STGAN) to remove clouds. We train our model
on a new large-scale spatiotemporal dataset that we construct, containing 97640
image pairs covering all continents. We demonstrate experimentally that the
proposed STGAN model outperforms standard models and can generate realistic
cloud-free images with high PSNR and SSIM values across a variety of
atmospheric conditions, leading to improved performance in downstream tasks
such as land cover classification.
