Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques-Reference-Cited by-同舟云学术

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

Published:2018-06-06 Issue:10 Volume:32 Page:2076-2097
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

He Jialv¹^ORCID,Li Xia²¹^ORCID,Yao Yao³⁴^ORCID,Hong Ye⁵^ORCID,Jinbao Zhang¹^ORCID

Affiliation:

1. School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, China

2. School of Geographic Sciences, Key Lab. of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, P.R. China

3. School of Information Engineering, China University of Geosciences, Wuhan, China

4. Department of Data Technology and Products, Alibaba Group, Hangzhou, China

5. Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zürich, Switzerland

Funder

Key National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2018.1480783

Reference57 articles.

1. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion

2. Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale

3. Estimating the relationship between urban forms and energy consumption: A case study in the Pearl River Delta, 2005–2008

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