  In recent years, considerable advancements have been made in the area of
Generative Adversarial Networks (GANs), particularly with the advent of
style-based architectures that address many key shortcomings - both in terms of
modeling capabilities and network interpretability. Despite these improvements,
the adoption of such approaches in the domain of satellite imagery is not
straightforward. Typical vision datasets used in generative tasks are
well-aligned and annotated, and exhibit limited variability. In contrast,
satellite imagery exhibits great spatial and spectral variability, wide
presence of fine, high-frequency details, while the tedious nature of
annotating satellite imagery leads to annotation scarcity - further motivating
developments in unsupervised learning. In this light, we present the first
pre-trained style- and wavelet-based GAN model that can readily synthesize a
wide gamut of realistic satellite images in a variety of settings and
conditions - while also preserving high-frequency information. Furthermore, we
show that by analyzing the intermediate activations of our network, one can
discover a multitude of interpretable semantic directions that facilitate the
guided synthesis of satellite images in terms of high-level concepts (e.g.,
urbanization) without using any form of supervision. Via a set of qualitative
and quantitative experiments we demonstrate the efficacy of our framework, in
terms of suitability for downstream tasks (e.g., data augmentation), quality of
synthetic imagery, as well as generalization capabilities to unseen datasets.
