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.
Renato Cardoso (CERN)
SOFIA VALLECORSA (CERN)
Edoardo Nemni (United Nations Satellite Centre (UNOSAT))
Edoardo Nemni is a Machine Learning Researcher at the United Nations Institute of Training and Research Operational Satellite Application Programme (UNITAR-UNOSAT). His research focus lies on apply deep learning algorithms to satellite imagery for disaster response such as satellite-derived flood analysis, shelter mapping, building footprints, damage assessment, and more. His current project is FloodAI: an end-to-end fully automated pipeline whereby satellite images of flood-prone areas are automatically downloaded and processed to output disaster maps.
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