StreetGAN: Towards Road Network Synthesis with Generative Adversarial Networks

We propose a novel example-based approach for road network synthesis relying on Generative Adversarial Networks (GANs), a recently introduced deep learning technique. In a pre-processing step, we first convert a given representation of a road network patch into a binary image where pixel intensities encode the presence or absence of streets. We then train a GAN that is able to automatically synthesize a multitude of arbitrary sized street networks that faithfully reproduce the style of the original patch. In a post-processing step, we extract a graph-based representation from the generated images. In contrast to other methods, our approach does neither require domainspecific expert knowledge, nor is it restricted to a limited number of street network templates. We demonstrate the general feasibility of our approach by synthesizing street networks of largely varying style and evaluate the results in terms of visual similarity as well as statistical similarity based on road network similarity measures.