License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.COSIT.2019.27
URN: urn:nbn:de:0030-drops-111193
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/11119/
Go to the corresponding LIPIcs Volume Portal

Jonietz, David ; Kopp, Michael

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

pdf-format:
LIPIcs-COSIT-2019-27.pdf (0.6 MB)

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.

BibTeX - Entry

@InProceedings{jonietz_et_al:LIPIcs:2019:11119,
  author =	{David Jonietz and Michael Kopp},
  title =	{{Towards Modeling Geographical Processes with Generative Adversarial Networks (GANs) (Short Paper)}},
  booktitle =	{14th International Conference on Spatial Information Theory (COSIT 2019)},
  pages =	{27:1--27:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-115-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{142},
  editor =	{Sabine Timpf and Christoph Schlieder and Markus Kattenbeck and Bernd Ludwig and Kathleen Stewart},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/11119},
  URN =		{urn:nbn:de:0030-drops-111193},
  doi =		{10.4230/LIPIcs.COSIT.2019.27},
  annote =	{Keywords: GAN, generative modeling, deep learning, geosimulation, game of life}
}

Keywords: GAN, generative modeling, deep learning, geosimulation, game of life
Collection: 14th International Conference on Spatial Information Theory (COSIT 2019)
Issue Date: 2019
Date of publication: 03.09.2019

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI