  Novel deep-learning (DL) architectures have reached a level where they can
generate digital media, including photorealistic images, that are difficult to
distinguish from real data. These technologies have already been used to
generate training data for Machine Learning (ML) models, and large
text-to-image models like DALL-E 2, Imagen, and Stable Diffusion are achieving
remarkable results in realistic high-resolution image generation. Given these
developments, issues of data authentication in monitoring and verification
deserve a careful and systematic analysis: How realistic are synthetic images?
How easily can they be generated? How useful are they for ML researchers, and
what is their potential for Open Science? In this work, we use novel DL models
to explore how synthetic satellite images can be created using conditioning
mechanisms. We investigate the challenges of synthetic satellite image
generation and evaluate the results based on authenticity and state-of-the-art
metrics. Furthermore, we investigate how synthetic data can alleviate the lack
of data in the context of ML methods for remote-sensing. Finally we discuss
implications of synthetic satellite imagery in the context of monitoring and
verification.
