  Image generation and image completion are rapidly evolving fields, thanks to
machine learning algorithms that are able to realistically replace missing
pixels. However, generating large high resolution images, with a large level of
details, presents important computational challenges. In this work, we
formulate the image generation task as completion of an image where one out of
three corners is missing. We then extend this approach to iteratively build
larger images with the same level of detail. Our goal is to obtain a scalable
methodology to generate high resolution samples typically found in satellite
imagery data sets. We introduce a conditional progressive Generative
Adversarial Networks (GAN), that generates the missing tile in an image, using
as input three initial adjacent tiles encoded in a latent vector by a
Wasserstein auto-encoder. We focus on a set of images used by the United
Nations Satellite Centre (UNOSAT) to train flood detection tools, and validate
the quality of synthetic images in a realistic setup.
