If you use this dataset in your research or applications, please refer to this source:
Kang, Y., Gao, S. and Roth, R.E., 2019. Transferring multiscale map styles using generative adversarial networks. International Journal of Cartography, 5(2-3), pp.115-141.
@article{kang2019transferring,
title={Transferring multiscale map styles using generative adversarial networks},
author={Kang, Yuhao and Gao, Song and Roth, Robert E},
journal={International Journal of Cartography},
volume={5},
number={2-3},
pages={115--141},
year={2019},
publisher={Taylor \& Francis}
}
The datasets are generated from Google Maps and OpenStreetMaps.
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 great potential for multiscale map style transferring, but many challenges remain requiring future research.