Document Open Access Logo

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

Authors David Jonietz, Michael Kopp

PDF
Thumbnail PDF

File

LIPIcs.COSIT.2019.27.pdf
  • Filesize: 0.55 MB
  • 9 pages

Document Identifiers

Author Details

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

Cite AsGet BibTex

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)
https://doi.org/10.4230/LIPIcs.COSIT.2019.27

Abstract

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
Keywords
  • GAN
  • generative modeling
  • deep learning
  • geosimulation
  • game of life

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Adrian Albert, Emanuele Strano, Jasleen Kaur, and Marta González. Modeling urbanization patterns with generative adversarial networks. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pages 2095-2098. IEEE, 2018. Google Scholar
  2. Marc P Armstrong. Temporality in spatial databases. In GIS/LIS 88 Proceedings: Accessing the World, pages 880-889, 1988. Google Scholar
  3. Itzhak Benenson, Paul M Torrens, and Paul Torrens. Geosimulation: Automata-based modeling of urban phenomena. John Wiley & Sons, 2004. Google Scholar
  4. Claudio EC Campelo, Brandon Bennett, and Vania Dimitrova. Identifying geographical processes from time-stamped data. In International Conference on GeoSpatial Sematics, pages 70-87. Springer, 2011. Google Scholar
  5. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in neural information processing systems, pages 2172-2180, 2016. Google Scholar
  6. Christophe Claramunt and Marius Theriault. Toward semantics for modelling spatio-temporal processes within GIS. Advances in GIs Research I, pages 27-43, 1996. Google Scholar
  7. Martin Gardener. MATHEMATICAL GAMES: The fantastic combinations of John Conway’s new solitaire game "life". Scientific American, 223:120-123, 1970. Google Scholar
  8. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672-2680, 2014. Google Scholar
  9. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Google Scholar
  10. Arief Koesdwiady and Fakhri Karray. Improving Multi-Step Traffic Flow Prediction, 2018. URL: http://arxiv.org/abs/1803.01365.
  11. Patrice Langlois. Simulation of complex systems in GIS. John Wiley & Sons, 2013. Google Scholar
  12. Yann LeCun and Corinna Cortes. MNIST handwritten digit database, 2010. URL: http://yann.lecun.com/exdb/mnist/.
  13. Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine. Stochastic adversarial video prediction. arXiv preprint arXiv:1804.01523, 2018. Google Scholar
  14. Linchao Li, Shanglu He, Fan Yang, Jian Zhang, and Bin. Ran. Space-mean-speed Prediction Based on the Fusion of Multi-source Data. In TRB 96th Annual Meeting Compendium of Papers, 2017. Google Scholar
  15. Xiaodan Liang, Lisa Lee, Wei Dai, and Eric P Xing. Dual motion GAN for future-flow embedded video prediction. In Proceedings of the IEEE International Conference on Computer Vision, pages 1744-1752, 2017. Google Scholar
  16. Yunyi Liang, Zhiyong Cui, Yu Tian, Huimiao Chen, and Yinhai Wang. A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic-State Estimation. Transportation Research Record, page 0361198118798737, 2018. Google Scholar
  17. Andrew L Maas, Awni Y Hannun, and Andrew Y Ng. Rectifier nonlinearities improve neural network acoustic models. In Proc. icml, volume 30, page 3, 2013. Google Scholar
  18. Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014. Google Scholar
  19. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In Advances in neural information processing systems, pages 2234-2242, 2016. Google Scholar
  20. Galit Shmueli et al. To explain or to predict? Statistical science, 25(3):289-310, 2010. Google Scholar
  21. Willington Siabato, Christophe Claramunt, Sergio Ilarri, and Miguel Ángel Manso-Callejo. A Survey of Modelling Trends in Temporal GIS. ACM Computing Surveys (CSUR), 51(2):30, 2018. Google Scholar
  22. Michael Simkin. The hunting of the New Herschel Conduits, 2015. URL: http://conwaylife.com/forums/viewtopic.php?f=2&t=1599&start=200#p19125.
  23. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929-1958, 2014. Google Scholar
  24. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008, 2017. Google Scholar
  25. Andrew J. Wade. Gemini, 2010. URL: http://conwaylife.com/w/index.php?title=Gemini.
  26. SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems, pages 802-810, 2015. Google Scholar
  27. Yang Zhang, Xiang Li, and Qianyu Zhang. Road Topology Refinement via a Multi-Conditional Generative Adversarial Network. Sensors, 19(5):1162, 2019. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact