Transferring Multiscale Map Styles Using Generative Adversarial Networks
This work addresses the problem of automating map style transfer for cartographers and GIS users, but it is incremental as it builds on existing GAN methods without major breakthroughs.
The authors tackled the problem of transferring stylistic design criteria from existing maps and visual art to unstylized GIS vector data across multiple map scales using generative adversarial networks (GANs), with results indicating GANs have great potential for this task but many challenges remain.
The advancement of the Artificial Intelligence (AI) technologies makes it possible to learn stylistic design criteria from existing maps or other visual art and transfer these styles to make new digital maps. In this paper, we propose a novel framework using AI for map style transfer applicable across multiple map scales. Specifically, we identify and transfer the stylistic elements from a target group of visual examples, including Google Maps, OpenStreetMap, and artistic paintings, to unstylized GIS vector data through two generative adversarial network (GAN) models. We then train a binary classifier based on a deep convolutional neural network to evaluate whether the transfer styled map images preserve the original map design characteristics. Our experiment results show that GANs have a great potential for multiscale map style transferring, but many challenges remain requiring future research.