Synthetic terrain realism is critical in VR applications based on computer graphics (e.g., games, simulations). Although fast procedural algorithms for automated terrain generation do exist, they still require human effort. This paper proposes a novel approach to procedural terrain generation, relying on Generative Adversarial Networks (GANs). The neural model is trained using terrestrial Points-of-Interest (PoIs, described by their geodesic coordinates/altitude) and publicly available

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... ding satellite images. After training is complete, the GAN can be employed for deriving realistic terrain images on-the- fly, by merely forwarding through it a rough 2D scatter plot of desired PoIs in image form (so-called "altitude image"). We demonstrate that such a GAN is able to translate this rough, quickly produced sketch into an actual photorealistic terrain image. Additionally, we describe a strategy for enhancing the visual diversity of trained model synthetic output images, by tweaking input altitude image orientation during GAN training. Finally, we perform an objective and a subjective evaluation of the proposed method. Results validate the latter's ability to rapidly create life-like terrain images from minimal input data.