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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:

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification
Shahab Jozdani, Brian Johnson, Dongmei Chen
Remote Sensing (2019) Vol. 11, Iss. 14, pp. 1713-1713
Open Access | Times Cited: 182

Showing 1-25 of 182 citing articles:

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
Mohammadreza Sheykhmousa, Masoud Mahdianpari, Hamid Ghanbari, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2020) Vol. 13, pp. 6308-6325
Open Access | Times Cited: 747
A unified deep learning framework for urban functional zone extraction based on multi-source heterogeneous data
Weipeng Lu, Chao Tao, Haifeng Li, et al.
Remote Sensing of Environment (2022) Vol. 270, pp. 112830-112830
Closed Access | Times Cited: 88
Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery
A. Narmilan, Felipe González, Arachchige Surantha Ashan Salgadoe, et al.
Remote Sensing (2022) Vol. 14, Iss. 5, pp. 1140-1140
Open Access | Times Cited: 83
Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions
Mohammed Abdulmajeed Moharram, Divya Meena Sundaram
Neurocomputing (2023) Vol. 536, pp. 90-113
Closed Access | Times Cited: 81
Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting
Md. Sharafat Chowdhury
Environmental Challenges (2023) Vol. 14, pp. 100800-100800
Open Access | Times Cited: 80
A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing
Yanzhao Wang, Yonghua Sun, X. L. Cao, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 206, pp. 311-334
Closed Access | Times Cited: 65
A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability
V V Bukhtoyarov, В С Тынченко, Vladimir A. Nelyub, et al.
Electronics (2023) Vol. 12, Iss. 1, pp. 215-215
Open Access | Times Cited: 54
Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability
Danish Raza, Hong Shu, Muhsan Ehsan, et al.
Cogent Food & Agriculture (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 4
Predictive modeling of air quality in the Tehran megacity via deep learning techniques
Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Abbas Pak, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 2
Studying spatial-temporal changes and relationship of land cover and surface Urban Heat Island derived through remote sensing in Yerevan, Armenia
Garegin Tepanosyan, Vahagn Muradyan, Azatuhi Hovsepyan, et al.
Building and Environment (2020) Vol. 187, pp. 107390-107390
Closed Access | Times Cited: 93
UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection
Hafiz Suliman Munawar, Fahim Ullah, Siddra Qayyum, et al.
Sustainability (2021) Vol. 13, Iss. 14, pp. 7547-7547
Open Access | Times Cited: 90
DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data
Hanqing Bao, Dongping Ming, Ya Guo, et al.
Remote Sensing (2020) Vol. 12, Iss. 7, pp. 1088-1088
Open Access | Times Cited: 82
Space-Based Earth Observations for Disaster Risk Management
Gonéri Le Cozannet, Matthieu Kervyn, Simone Russo, et al.
Surveys in Geophysics (2020) Vol. 41, Iss. 6, pp. 1209-1235
Open Access | Times Cited: 72
Geographic Object-Based Image Analysis: A Primer and Future Directions
Maja Kucharczyk, Geoffrey J. Hay, Salar Ghaffarian, et al.
Remote Sensing (2020) Vol. 12, Iss. 12, pp. 2012-2012
Open Access | Times Cited: 71
Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping
Anca Dabija, Marcin Kluczek, Bogdan Zagajewski, et al.
Remote Sensing (2021) Vol. 13, Iss. 4, pp. 777-777
Open Access | Times Cited: 71
A review and meta-analysis of Generative Adversarial Networks and their applications in remote sensing
Shahab Jozdani, Dongmei Chen, Darren Pouliot, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 108, pp. 102734-102734
Open Access | Times Cited: 64
Classifying urban functional regions by integrating buildings and points-of-interest using a stacking ensemble method
Min Yang, Bo Kong, Ruirong Dang, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 108, pp. 102753-102753
Open Access | Times Cited: 42
Urban land use and land cover classification with interpretable machine learning – A case study using Sentinel-2 and auxiliary data
Benyamin Hosseiny, Abdulhakim M. Abdi, Sadegh Jamali
Remote Sensing Applications Society and Environment (2022) Vol. 28, pp. 100843-100843
Open Access | Times Cited: 39
LULC changes to riverine flooding: A case study on the Jamuna River, Bangladesh using the multilayer perceptron model
Md Mehedi Hasan, Md Sahjalal Mondol Nilay, Nahid Hossain Jibon, et al.
Results in Engineering (2023) Vol. 18, pp. 101079-101079
Open Access | Times Cited: 26
Utilizing Multitemporal Indices and Spectral Bands of Sentinel-2 to Enhance Land Use and Land Cover Classification with Random Forest and Support Vector Machine
Atefe Arfa, Masoud Minaei
Advances in Space Research (2024) Vol. 74, Iss. 11, pp. 5580-5590
Closed Access | Times Cited: 10
M 2 Caps: learning multi-modal capsules of optical and SAR images for land cover classification
Haodi Zhang, Anzhu Yu, Kuiliang Gao, et al.
International Journal of Digital Earth (2025) Vol. 18, Iss. 1
Open Access | Times Cited: 1
Land-Use Land-Cover Dynamics and Future Projections Using GEE, ML, and QGIS-MOLUSCE: A Case Study in Manisa
Halil İbrahim Gündüz
Sustainability (2025) Vol. 17, Iss. 4, pp. 1363-1363
Open Access | Times Cited: 1
Bare ground classification using a spectral index ensemble and machine learning models optimized across 12 international study sites
Sarah J. Becker, Megan C. Maloney, A. Griffin, et al.
Geocarto International (2025) Vol. 40, Iss. 1
Open Access | Times Cited: 1
Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-classification Comparison (PCC)
Guanyao Xie, Simona Niculescu
Remote Sensing (2021) Vol. 13, Iss. 19, pp. 3899-3899
Open Access | Times Cited: 54
A comparative analysis of different pixel and object-based classification algorithms using multi-source high spatial resolution satellite data for LULC mapping
Akanksha Balha, Javed Mallick, Suneel Pandey, et al.
Earth Science Informatics (2021) Vol. 14, Iss. 4, pp. 2231-2247
Closed Access | Times Cited: 47
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