  Cloud detection is a pivotal satellite image pre-processing step that can be
performed both on the ground and on board a satellite to tag useful images. In
the latter case, it can help to reduce the amount of data to downlink by
pruning the cloudy areas, or to make a satellite more autonomous through
data-driven acquisition re-scheduling of the cloudy areas. We approach this
important task with nnU-Nets, a self-reconfigurable framework able to perform
meta-learning of a segmentation network over various datasets. Our experiments,
performed over Sentinel-2 and Landsat-8 multispectral images revealed that
nnU-Nets deliver state-of-the-art cloud segmentation performance without any
manual design. Our approach was ranked within the top 7% best solutions (across
847 participating teams) in the On Cloud N: Cloud Cover Detection Challenge,
where we reached the Jaccard index of 0.882 over more than 10k unseen
Sentinel-2 image patches (the winners obtained 0.897, whereas the baseline
U-Net with the ResNet-34 backbone used as an encoder: 0.817, and the classic
Sentinel-2 image thresholding: 0.652).
