  In recent years, advances in machine learning algorithms, cheap computational
resources, and the availability of big data have spurred the deep learning
revolution in various application domains. In particular, supervised learning
techniques in image analysis have led to superhuman performance in various
tasks, such as classification, localization, and segmentation, while
unsupervised learning techniques based on increasingly advanced generative
models have been applied to generate high-resolution synthetic images
indistinguishable from real images.
  In this paper we consider a state-of-the-art machine learning model for image
inpainting, namely a Wasserstein Generative Adversarial Network based on a
fully convolutional architecture with a contextual attention mechanism. We show
that this model can successfully be transferred to the setting of digital
elevation models (DEMs) for the purpose of generating semantically plausible
data for filling voids. Training, testing and experimentation is done on
GeoTIFF data from various regions in Norway, made openly available by the
Norwegian Mapping Authority.
