GANmapper: geographical data translation

Abstract

We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data.

Keywords:

• Deep learning
• machine learning
• cartography
• GIScience
• GeoAI

Acknowledgements

We gratefully acknowledge the input data used in this research and the valuable comments by the editor and the reviewers. We thank the members of the NUS Urban Analytics Lab for the discussions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The code that support the findings of this study are available on Github at https://github.com/ualsg/GANmapper. A version with trained checkpoints is available at https://doi.org/10.6084/m9.figshare.15103128

Notes

1 Notes: https://www.ura.gov.sg/Corporate/Planning/Master-Plan; last accessed on 28 July 2021.

Additional information

Funding

This research is part of the project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the Start Up Grant R-295-000-171-133.

Notes on contributors

Abraham Noah Wu

Abraham Noah Wu is a research assistant at the National University of Singapore. He holds a Master Degree in Architecture from the National University of Singapore.

Filip Biljecki

Filip Biljecki is an assistant professor at the National University of Singapore and the principal investigator of the NUS Urban Analytics Lab. He holds an MSc in Geomatics and a PhD in 3D GIS from the Delft University of Technology in the Netherlands.