# Transferring Multiscale Map Styles Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1905.02200 · HTML: https://mlsanity.com/paper/1905.02200/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

## Built on by (most cited first, 11 of 11)

- [TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning](https://mlsanity.com/paper/2406.15658/) (2024)
- [RNGDet: Road Network Graph Detection by Transformer in Aerial Images](https://mlsanity.com/paper/2202.07824/) (2022)
- [Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach](https://mlsanity.com/paper/2512.20056/) (2025)
- [GANmapper: geographical data translation](https://mlsanity.com/paper/2108.04232/) (2021)
- [Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics](https://mlsanity.com/paper/2312.07901/) (2023)
- [CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation](https://mlsanity.com/paper/2505.09936/) (2025)
- [How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective](https://mlsanity.com/paper/2311.07126/) (2023)
- [Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models](https://mlsanity.com/paper/2505.24260/) (2025)
- [Generative AI in Map-Making: A Technical Exploration and Its Implications for Cartographers](https://mlsanity.com/paper/2508.18959/) (2025)
- [Runway Extraction and Improved Mapping from Space Imagery](https://mlsanity.com/paper/2201.00848/) (2021)
- [Generalizable Multiscale Segmentation of Heterogeneous Map Collections](https://mlsanity.com/paper/2603.05037/) (2026)

## Abstract (arXiv)

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.