Towards Modeling Geographical Processes with Generative Adversarial Networks (GANs) (Short Paper)

Authors David Jonietz, Michael Kopp

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David Jonietz
  • HERE Technologies, Switzerland
Michael Kopp
  • HERE Technologies, Switzerland
  • Institute of Advanced Research in Artificial Intelligence (IARAI), Austria

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David Jonietz and Michael Kopp. Towards Modeling Geographical Processes with Generative Adversarial Networks (GANs) (Short Paper). In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 27:1-27:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Recently, Generative Adversarial Networks (GANs) have demonstrated great potential for a range of Machine Learning tasks, including synthetic video generation, but have so far not been applied to the domain of modeling geographical processes. In this study, we align these two problems and - motivated by the potential advantages of GANs compared to traditional geosimulation methods - test the capability of GANs to learn a set of underlying rules which determine a geographical process. For this purpose, we turn to Conway’s well-known Game of Life (GoL) as a source for spatio-temporal training data, and further argue for its (and simple variants of it) usefulness as a potential standard training data set for benchmarking generative geographical process models.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Neural networks
  • GAN
  • generative modeling
  • deep learning
  • geosimulation
  • game of life


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