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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:

Combining spatial response features and machine learning classifiers for landslide susceptibility mapping
Ruilong Wei, Chengming Ye, Tianbo Sui, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 107, pp. 102681-102681
Open Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
Moziihrii Ado, Khwairakpam Amitab, Arnab Kumar Maji, et al.
Remote Sensing (2022) Vol. 14, Iss. 13, pp. 3029-3029
Open Access | Times Cited: 109
Towards Human-centric Digital Twins: Leveraging Computer Vision and Graph Models to Predict Outdoor Comfort
Pengyuan Liu, Tianhong Zhao, Junjie Luo, et al.
Sustainable Cities and Society (2023) Vol. 93, pp. 104480-104480
Closed Access | Times Cited: 53
A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment
A. Habibi, M. R. Delavar, Mohammad Sadegh Sadeghian, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 122, pp. 103401-103401
Open Access | Times Cited: 42
Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping
Yongze Song, Margaret Kalácska, Mateo Gašparović, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 120, pp. 103300-103300
Open Access | Times Cited: 40
Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia
Fahad Alshehri, Atiqur Rahman
Water (2023) Vol. 15, Iss. 12, pp. 2298-2298
Open Access | Times Cited: 29
A dual-encoder U-Net for landslide detection using Sentinel-2 and DEM data
Wei Lü, Dafang Zhuang, Zuopei Zhang, et al.
Landslides (2023) Vol. 20, Iss. 9, pp. 1975-1987
Closed Access | Times Cited: 28
An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features
Yi He, Zhan’ao Zhao, Qing Zhu, et al.
International Journal of Digital Earth (2023) Vol. 17, Iss. 1
Open Access | Times Cited: 26
Spatial+: A new cross-validation method to evaluate geospatial machine learning models
Yanwen Wang, Mahdi Khodadadzadeh, R. Zurita‐Milla
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 121, pp. 103364-103364
Open Access | Times Cited: 24
Landslide Risk Evaluation in Shenzhen Based on Stacking Ensemble Learning and InSAR
Binghai Gao, Yi He, Xueye Chen, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 1-18
Open Access | Times Cited: 24
Landslide Susceptibility Mapping Considering Landslide Local-Global Features Based on CNN and Transformer
Zeyang Zhao, Tao Chen, Jie Dou, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024) Vol. 17, pp. 7475-7489
Open Access | Times Cited: 12
Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review
Stephen Akosah, Ivan Gratchev, Donghyun Kim, et al.
Remote Sensing (2024) Vol. 16, Iss. 16, pp. 2947-2947
Open Access | Times Cited: 9
Integrating a multi-dimensional deep convolutional neural network with optimized sample selection for landslide susceptibility assessment
Yueyue Wang, Xueling Wu, Kun Zou, et al.
Geo-spatial Information Science (2025), pp. 1-21
Open Access | Times Cited: 1
Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework
Krishnagopal Halder, Amit Kumar Srivastava, Anitabha Ghosh, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques
Zuleide Alves Ferreira, Bruna Almeida, Ana Cristina Costa, et al.
Geomatics Natural Hazards and Risk (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 1
Evaluation of neural network models for landslide susceptibility assessment
Yaning Yi, Wanchang Zhang, Xiwei Xu, et al.
International Journal of Digital Earth (2022) Vol. 15, Iss. 1, pp. 934-953
Open Access | Times Cited: 35
Landslide susceptibility mapping using automatically constructed CNN architectures with pre-slide topographic DEM of deep-seated catastrophic landslides caused by Typhoon Talas
Teruyuki Kikuchi, Koki Sakita, Satoshi Nishiyama, et al.
Natural Hazards (2023) Vol. 117, Iss. 1, pp. 339-364
Open Access | Times Cited: 21
A Computational Geospatial Approach to Assessing Land-Use Compatibility in Urban Planning
Omid Mansourihanis, Mohammad Javad Maghsoodi Tilaki, Samira Yousefian, et al.
Land (2023) Vol. 12, Iss. 11, pp. 2083-2083
Open Access | Times Cited: 21
Comparison of bias-corrected multisatellite precipitation products by deep learning framework
Xuan-Hien Le, Linh Nguyen Van, Duc Hai Nguyen, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 116, pp. 103177-103177
Open Access | Times Cited: 17
Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities
Jing Jia, Wenjie Ye
Remote Sensing (2023) Vol. 15, Iss. 16, pp. 4098-4098
Open Access | Times Cited: 17
Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be
Zhice Fang, Yi Wang, C.J. van Westen, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 126, pp. 103631-103631
Open Access | Times Cited: 17
A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network
Qing Zhang, Yi He, Lifeng Zhang, et al.
Gondwana Research (2024) Vol. 132, pp. 323-342
Closed Access | Times Cited: 7
A comparative study of different neural network models for landslide susceptibility mapping
Zhan'ao Zhao, Yi He, Sheng Yao, et al.
Advances in Space Research (2022) Vol. 70, Iss. 2, pp. 383-401
Closed Access | Times Cited: 28
Lithological mapping of geological remote sensing via adversarial semi-supervised segmentation network
Sheng Wang, Xiaohui Huang, Wei Han, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 125, pp. 103536-103536
Open Access | Times Cited: 15
Generating accurate negative samples for landslide susceptibility mapping: A combined self-organizing-map and one-class SVM method
Chengming Ye, Rong Tang, Ruilong Wei, et al.
Frontiers in Earth Science (2023) Vol. 10
Open Access | Times Cited: 13
CResU-Net: a method for landslide mapping using deep learning
Thang M Pham, Nam Do, Ha T T Pham, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 035008-035008
Open Access | Times Cited: 5
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