  In this paper we propose a mask-conditional synthetic image generation model
for creating synthetic satellite imagery datasets. Given a dataset of real
high-resolution images and accompanying land cover masks, we show that it is
possible to train an upstream conditional synthetic imagery generator, use that
generator to create synthetic imagery with the land cover masks, then train a
downstream model on the synthetic imagery and land cover masks that achieves
similar test performance to a model that was trained with the real imagery.
Further, we find that incorporating a mixture of real and synthetic imagery
acts as a data augmentation method, producing better models than using only
real imagery (0.5834 vs. 0.5235 mIoU). Finally, we find that encouraging
diversity of outputs in the upstream model is a necessary component for
improved downstream task performance. We have released code for reproducing our
work on GitHub, see
https://github.com/ms-synthetic-satellite-image/synthetic-satellite-imagery .
