Integrating spatiotemporal co-evolution patterns of land types with cellular automata to enhance the reliability of land use projections-Reference-Cited by-同舟云学术

Integrating spatiotemporal co-evolution patterns of land types with cellular automata to enhance the reliability of land use projections

Published:2024-02-06 Issue:5 Volume:38 Page:956-980
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

He Zhanjun¹²³⁴,Wang Xubin¹,Liang Xun¹,Wu Liang¹,Yao Jing³

Affiliation:

1. School of Computer Science, China University of Geosciences (Wuhan), Wuhan, China

2. Artificical Intelligence School, Wuchang University of Technology, Wuhan, China

3. Urban Big Data Centre, University of Glasgow, Glasgow, Scotland

4. State Key Laboratory of Geo-Information Engineering, Xi’an, China

Funder

National Natural Science Foundation of China

“CUG Scholar” Scientific Research Funds at China University of Geosciences

State Key Laboratory of Geo-Information Engineering

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2024.2314575

Reference51 articles.

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3. Uncertainty Reduction of Unlabeled Features in Landslide Inventory Using Machine Learning t-SNE Clustering and Data Mining Apriori Association Rule Algorithms

4. Modelling spatial association in pattern based land use simulation models

5. From Cells to Cities

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