  Artifacts in imagery captured by remote sensing, such as clouds, snow, and
shadows, present challenges for various tasks, including semantic segmentation
and object detection. A primary challenge in developing algorithms for
identifying such artifacts is the cost of collecting annotated training data.
In this work, we explore how recent advances in multi-image fusion can be
leveraged to bootstrap single image cloud detection. We demonstrate that a
network optimized to estimate image quality also implicitly learns to detect
clouds. To support the training and evaluation of our approach, we collect a
large dataset of Sentinel-2 images along with a per-pixel semantic labelling
for land cover. Through various experiments, we demonstrate that our method
reduces the need for annotated training data and improves cloud detection
performance.
