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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:

Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms
Andrea Tassi, Marco Vizzari
Remote Sensing (2020) Vol. 12, Iss. 22, pp. 3776-3776
Open Access | Times Cited: 277

Showing 1-25 of 277 citing articles:

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation
Salvatore Praticò, Francesco Solano, Salvatore Di Fazio, et al.
Remote Sensing (2021) Vol. 13, Iss. 4, pp. 586-586
Open Access | Times Cited: 172
Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review
Liping Yang, Joshua Driscol, Sarigai Sarigai, et al.
Remote Sensing (2022) Vol. 14, Iss. 14, pp. 3253-3253
Open Access | Times Cited: 146
Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods
Vahid Nasiri, Azade Deljouei, Fardin Moradi, et al.
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 1977-1977
Open Access | Times Cited: 145
Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India
Kotapati Narayana Loukika, K. Venkata Reddy, Venkataramana Sridhar
Sustainability (2021) Vol. 13, Iss. 24, pp. 13758-13758
Open Access | Times Cited: 111
Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques
Sana Basheer, Xiuquan Wang, Aitazaz A. Farooque, et al.
Remote Sensing (2022) Vol. 14, Iss. 19, pp. 4978-4978
Open Access | Times Cited: 111
Land use/land cover and change detection mapping in Rahuri watershed area (MS), India using the google earth engine and machine learning approach
Chaitanya B. Pande
Geocarto International (2022) Vol. 37, Iss. 26, pp. 13860-13880
Closed Access | Times Cited: 89
Interannual changes of coastal aquaculture ponds in China at 10-m spatial resolution during 2016–2021
Ming Wang, Dehua Mao, Xiangming Xiao, et al.
Remote Sensing of Environment (2022) Vol. 284, pp. 113347-113347
Closed Access | Times Cited: 78
Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India
Pir Mohammad, Ajanta Goswami, Sarthak Chauhan, et al.
Urban Climate (2022) Vol. 42, pp. 101116-101116
Closed Access | Times Cited: 77
Integrating forest cover change and carbon storage dynamics: Leveraging Google Earth Engine and InVEST model to inform conservation in hilly regions
Abdulla ‐ Al Kafy, Milan Saha, Md. Abdul Fattah, et al.
Ecological Indicators (2023) Vol. 152, pp. 110374-110374
Open Access | Times Cited: 59
Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples
Kai Cheng, Yanjun Su, Hongcan Guan, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 196, pp. 356-371
Closed Access | Times Cited: 54
Wetland mapping in East Asia by two-stage object-based Random Forest and hierarchical decision tree algorithms on Sentinel-1/2 images
Ming Wang, Dehua Mao, Yeqiao Wang, et al.
Remote Sensing of Environment (2023) Vol. 297, pp. 113793-113793
Closed Access | Times Cited: 50
Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery
Mohammad Ganjirad, Hossein Bagheri
Ecological Informatics (2024) Vol. 80, pp. 102498-102498
Open Access | Times Cited: 19
Novel approach for the LULC change detection using GIS & Google Earth Engine through spatiotemporal analysis to evaluate the urbanization growth of Ahmedabad city
Anant Patel, Daivee Vyas, Nirali Chaudhari, et al.
Results in Engineering (2024) Vol. 21, pp. 101788-101788
Open Access | Times Cited: 18
Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation
Chaitanya B. Pande, Aman Srivastava, Kanak N. Moharir, et al.
Environmental Sciences Europe (2024) Vol. 36, Iss. 1
Open Access | Times Cited: 16
Assessment of urban thermal field variance index and defining the relationship between land cover and surface temperature in Chattogram city: A remote sensing and statistical approach
Md. Nazmul Huda Naim, Abdulla ‐ Al Kafy
Environmental Challenges (2021) Vol. 4, pp. 100107-100107
Open Access | Times Cited: 94
Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine
Li Pan, Haoming Xia, Xiaoyang Zhao, et al.
Remote Sensing (2021) Vol. 13, Iss. 13, pp. 2510-2510
Open Access | Times Cited: 92
Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park
Andrea Tassi, Daniela Gigante, Giuseppe Modica, et al.
Remote Sensing (2021) Vol. 13, Iss. 12, pp. 2299-2299
Open Access | Times Cited: 87
Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India
Devanantham Abijith, Subbarayan Saravanan
Environmental Science and Pollution Research (2021) Vol. 29, Iss. 57, pp. 86055-86067
Closed Access | Times Cited: 77
Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine
Chong Luo, Beisong Qi, Huanjun Liu, et al.
Remote Sensing (2021) Vol. 13, Iss. 4, pp. 561-561
Open Access | Times Cited: 70
Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine
Timothy Mayer, Ate Poortinga, Biplov Bhandari, et al.
ISPRS Open Journal of Photogrammetry and Remote Sensing (2021) Vol. 2, pp. 100005-100005
Open Access | Times Cited: 70
Mapping Phenological Functional Types (PhFT) in the Indian Eastern Himalayas using machine learning algorithm in Google Earth Engine
Manoj Kumar, Sweta Nisha Phukon, Akshay Paygude, et al.
Computers & Geosciences (2021) Vol. 158, pp. 104982-104982
Closed Access | Times Cited: 66
PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine
Marco Vizzari
Remote Sensing (2022) Vol. 14, Iss. 11, pp. 2628-2628
Open Access | Times Cited: 59
Mapping the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat dense time series stack
Shuai Xie, Liangyun Liu, Xiao Zhang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2022) Vol. 185, pp. 201-218
Closed Access | Times Cited: 45
Automatic monitoring of surface water dynamics using Sentinel-1 and Sentinel-2 data with Google Earth Engine
Zhiheng Chen, Shuhe Zhao
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 113, pp. 103010-103010
Open Access | Times Cited: 45
A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine
Alireza Taheri Dehkordi, Mohammad Javad Valadan Zoej, Hani Ghasemi, et al.
Sustainability (2022) Vol. 14, Iss. 13, pp. 8046-8046
Open Access | Times Cited: 42
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