  In recent years, semantic segmentation has become a pivotal tool in
processing and interpreting satellite imagery. Yet, a prevalent limitation of
supervised learning techniques remains the need for extensive manual
annotations by experts. In this work, we explore the potential of generative
image diffusion to address the scarcity of annotated data in earth observation
tasks. The main idea is to learn the joint data manifold of images and labels,
leveraging recent advancements in denoising diffusion probabilistic models. To
the best of our knowledge, we are the first to generate both images and
corresponding masks for satellite segmentation. We find that the obtained pairs
not only display high quality in fine-scale features but also ensure a wide
sampling diversity. Both aspects are crucial for earth observation data, where
semantic classes can vary severely in scale and occurrence frequency. We employ
the novel data instances for downstream segmentation, as a form of data
augmentation. In our experiments, we provide comparisons to prior works based
on discriminative diffusion models or GANs. We demonstrate that integrating
generated samples yields significant quantitative improvements for satellite
semantic segmentation -- both compared to baselines and when training only on
the original data.
