A maps-to-maps approach for simulating urban land expansion based on convolutional long short-term memory neural networks-Reference-Cited by-同舟云学术

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A maps-to-maps approach for simulating urban land expansion based on convolutional long short-term memory neural networks

Published:2024-01-03 Issue:3 Volume:38 Page:503-526
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Zhou Zihao¹,Chen Yimin¹,Liu Xiaoping¹,Zhang Xinchang²,Zhang Honghui³

Affiliation:

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

2. School of Geography and Remote Sensing, Guangzhou University, Guangzhou, China

3. Guangdong Guodi Planning Science Technology Co., Ltd., Guangzhou, China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2023.2298296

Reference61 articles.

1. Integrating multi-criteria analysis and spherical cellular automata approach for modelling global urban land-use change

2. Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data

3. Path dependence and the validation of agent‐based spatial models of land use

4. MHA-Net: Multipath Hybrid Attention Network for Building Footprint Extraction From High-Resolution Remote Sensing Imagery

5. A grey wolf optimizer-cellular automata integrated model for urban growth simulation and optimization

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multiple Land-Use Simulations and Driving Factor Analysis by Integrating a Deep Cascade Forest Model and Cellular Automata: A Case Study in the Pearl River Delta, China;Remote Sensing;2024-07-27

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