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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Coupling cellular automata with area partitioning and spatiotemporal convolution for dynamic land use change simulation
Yuehui Qian, Weiran Xing, Xuefeng Guan, et al.
The Science of The Total Environment (2020) Vol. 722, pp. 137738-137738
Closed Access | Times Cited: 81

Showing 1-25 of 81 citing articles:

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Junye Wang, Michael Bretz, M. Ali Akber Dewan, et al.
The Science of The Total Environment (2022) Vol. 822, pp. 153559-153559
Closed Access | Times Cited: 233
Algorithmic urban planning for smart and sustainable development: Systematic review of the literature
Tim Heinrich Son, Zack Weedon, Tan Yiğitcanlar, et al.
Sustainable Cities and Society (2023) Vol. 94, pp. 104562-104562
Open Access | Times Cited: 174
Land space optimization of urban-agriculture-ecological functions in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration, China
Xiao Ouyang, Xu Jun, Jiayu Li, et al.
Land Use Policy (2022) Vol. 117, pp. 106112-106112
Closed Access | Times Cited: 69
Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
Quoc Bao Pham, Sk Ajim Ali, Farhana Parvin, et al.
Advances in Space Research (2024) Vol. 74, Iss. 1, pp. 17-47
Closed Access | Times Cited: 15
Land-N2N: An effective and efficient model for simulating the demand-driven changes in multifunctional lands
Yifan Gao, Changqing Song, Zhifeng Liu, et al.
Environmental Modelling & Software (2025) Vol. 185, pp. 106318-106318
Closed Access | Times Cited: 2
Machine learning in landscape ecological analysis: a review of recent approaches
Mihai‐Sorin Stupariu, Samuel A. Cushman, Alin Pleșoianu, et al.
Landscape Ecology (2021) Vol. 37, Iss. 5, pp. 1227-1250
Closed Access | Times Cited: 69
Predicting the surface urban heat island intensity of future urban green space development using a multi-scenario simulation
Jie Liu, Lang Zhang, Qingping Zhang, et al.
Sustainable Cities and Society (2021) Vol. 66, pp. 102698-102698
Closed Access | Times Cited: 57
Simulating mixed land-use change under multi-label concept by integrating a convolutional neural network and cellular automata: a case study of Huizhou, China
Xinxin Wu, Xiaoping Liu, Dachuan Zhang, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 609-632
Open Access | Times Cited: 51
Real-time image-based air quality estimation by deep learning neural networks
Pu-Yun Kow, I-Wen Hsia, Li‐Chiu Chang, et al.
Journal of Environmental Management (2022) Vol. 307, pp. 114560-114560
Closed Access | Times Cited: 39
A novel spatio-temporal cellular automata model coupling partitioning with CNN-LSTM to urban land change simulation
Ye Zhou, Chen Huang, Tao Wu, et al.
Ecological Modelling (2023) Vol. 482, pp. 110394-110394
Closed Access | Times Cited: 27
How can SHAP (SHapley Additive exPlanations) interpretations improve deep learning based urban cellular automata model?
Changlan Yang, Xuefeng Guan, Qingyang Xu, et al.
Computers Environment and Urban Systems (2024) Vol. 111, pp. 102133-102133
Closed Access | Times Cited: 8
Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata
Christoph Parsch, Benjamin Wagner, Jayden E. Engert, et al.
The Science of The Total Environment (2025) Vol. 970, pp. 178864-178864
Open Access | Times Cited: 1
Integrating machine learning with Markov chain and cellular automata models for modelling urban land use change
Onuwa Okwuashi, Christopher E. Ndehedehe
Remote Sensing Applications Society and Environment (2020) Vol. 21, pp. 100461-100461
Open Access | Times Cited: 54
Urban expansion simulation and development-oriented zoning of rapidly urbanising areas: A case study of Hangzhou
Ye Zhou, Tao Wu, Yechenzi Wang
The Science of The Total Environment (2021) Vol. 807, pp. 150813-150813
Open Access | Times Cited: 48
Modeling the response of ecological service value to land use change through deep learning simulation in Lanzhou, China
Jiamin Liu, Bin Xiao, Jizong Jiao, et al.
The Science of The Total Environment (2021) Vol. 796, pp. 148981-148981
Closed Access | Times Cited: 45
Simulating urban expansion using cellular automata model with spatiotemporally explicit representation of urban demand
Jianxin Yang, Wenwu Tang, Jian Gong, et al.
Landscape and Urban Planning (2022) Vol. 231, pp. 104640-104640
Closed Access | Times Cited: 28
Scenario modeling of ecosystem service trade-offs and bundles in a semi-arid valley basin
Jiamin Liu, Xiutong Pei, Wanyang Zhu, et al.
The Science of The Total Environment (2023) Vol. 896, pp. 166413-166413
Closed Access | Times Cited: 18
A cellular automata model coupled with partitioning CNN-LSTM and PLUS models for urban land change simulation
Chen Huang, Ye Zhou, Tao Wu, et al.
Journal of Environmental Management (2023) Vol. 351, pp. 119828-119828
Closed Access | Times Cited: 17
Simulating urban land growth by incorporating historical information into a cellular automata model
Haijun Wang, Jiaqi Guo, Bin Zhang, et al.
Landscape and Urban Planning (2021) Vol. 214, pp. 104168-104168
Closed Access | Times Cited: 33
An analysis of multiple ecosystem services in a large-scale urbanized area of northern China based on the food-energy-water integrative framework
Siyang Zhou, Wei Li, Zhonggui Lu, et al.
Environmental Impact Assessment Review (2022) Vol. 98, pp. 106913-106913
Closed Access | Times Cited: 27
Modeling dynamic land use changes in the eastern portion of the hexi corridor, China by cnn-gru hybrid model
Bin Xiao, Jiamin Liu, Jizong Jiao, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 501-519
Open Access | Times Cited: 23
Simulating large-scale urban land-use patterns and dynamics using the U-Net deep learning architecture
Jinzhu Wang, Michalis Hadjikakou, Richard J. Hewitt, et al.
Computers Environment and Urban Systems (2022) Vol. 97, pp. 101855-101855
Closed Access | Times Cited: 23
Does partition matter? A new approach to modeling land use change
Fei He, Jun Yang, Yuqing Zhang, et al.
Computers Environment and Urban Systems (2023) Vol. 106, pp. 102041-102041
Closed Access | Times Cited: 15
A hybrid cellular automaton model integrated with 3DCNN and LSTM for simulating land use/cover change
Wei Yang, Yu Zhang, Kun Hou, et al.
International Journal of Digital Earth (2025) Vol. 18, Iss. 1
Open Access
Monitoring and modeling vulnerability of land use changes in the current flood hazard conditions using novel hybrid GIS-based approaches and remote sensing data
Shadman Darvishi
Earth Science Informatics (2025) Vol. 18, Iss. 2
Closed Access
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