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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:

Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data
Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa, et al.
Ecological Modelling (2019) Vol. 406, pp. 109-120
Open Access | Times Cited: 424

Showing 1-25 of 424 citing articles:

Global prevalence of non-perennial rivers and streams
Mathis Messager, Bernhard Lehner, Charlotte Cockburn, et al.
Nature (2021) Vol. 594, Iss. 7863, pp. 391-397
Closed Access | Times Cited: 442
Machine learning for digital soil mapping: Applications, challenges and suggested solutions
Alexandre M.J.‐C. Wadoux, Budiman Minasny, Alex B. McBratney
Earth-Science Reviews (2020) Vol. 210, pp. 103359-103359
Open Access | Times Cited: 407
Predicting into unknown space? Estimating the area of applicability of spatial prediction models
Hanna Meyer, Edzer Pebesma
Methods in Ecology and Evolution (2021) Vol. 12, Iss. 9, pp. 1620-1633
Open Access | Times Cited: 356
Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction
Hanna Meyer, Christoph Reudenbach, Stephan Wöllauer, et al.
Ecological Modelling (2019) Vol. 411, pp. 108815-108815
Open Access | Times Cited: 328
Self-attention for raw optical Satellite Time Series Classification
Marc Rußwurm, Marco Körner
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 169, pp. 421-435
Open Access | Times Cited: 245
African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning
Tomislav Hengl, Matt Miller, Josip Križan, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 200
Machine learning and landslide studies: recent advances and applications
Faraz S. Tehrani, Michele Calvello, Zhongqiang Liu, et al.
Natural Hazards (2022) Vol. 114, Iss. 2, pp. 1197-1245
Open Access | Times Cited: 156
Automated detection of rock glaciers using deep learning and object-based image analysis
Benjamin Aubrey Robson, Tobias Bolch, Shelley MacDonell, et al.
Remote Sensing of Environment (2020) Vol. 250, pp. 112033-112033
Open Access | Times Cited: 153
National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data
Qigen Lin, Pedro Lima, Stefan Steger, et al.
Geoscience Frontiers (2021) Vol. 12, Iss. 6, pp. 101248-101248
Open Access | Times Cited: 125
Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects
Stefan Steger, Volkmar Mair, Christian Kofler, et al.
The Science of The Total Environment (2021) Vol. 776, pp. 145935-145935
Open Access | Times Cited: 122
Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning
Goutam Konapala, Sujay V. Kumar, S. Ahmad
ISPRS Journal of Photogrammetry and Remote Sensing (2021) Vol. 180, pp. 163-173
Open Access | Times Cited: 112
Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey
Muzaffer Can İban, Aliihsan Şekertekin
Ecological Informatics (2022) Vol. 69, pp. 101647-101647
Closed Access | Times Cited: 112
Spatially autocorrelated training and validation samples inflate performance assessment of convolutional neural networks
Teja Kattenborn, Felix Schiefer, Julian Frey, et al.
ISPRS Open Journal of Photogrammetry and Remote Sensing (2022) Vol. 5, pp. 100018-100018
Open Access | Times Cited: 83
Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility
Pedro Lima, Stefan Steger, Thomas Glade, et al.
Journal of Mountain Science (2022) Vol. 19, Iss. 6, pp. 1670-1698
Open Access | Times Cited: 81
Global patterns in urban green space are strongly linked to human development and population density
Rasmus Attrup Bille, Kristine Engemann Jensen, Robert Buitenwerf
Urban forestry & urban greening (2023) Vol. 86, pp. 127980-127980
Open Access | Times Cited: 59
Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm
Yubo Sun, Hao Cheng, Shizhe Zhang, et al.
Construction and Building Materials (2023) Vol. 385, pp. 131519-131519
Open Access | Times Cited: 58
Assessing and improving the transferability of current global spatial prediction models
Marvin Ludwig, Álvaro Moreno‐Martínez, Norbert Hölzel, et al.
Global Ecology and Biogeography (2023) Vol. 32, Iss. 3, pp. 356-368
Open Access | Times Cited: 54
Assessing water quality of an ecologically critical urban canal incorporating machine learning approaches
Abdul Majed Sajib, Mir Talas Mahammad Diganta, Md Moniruzzaman, et al.
Ecological Informatics (2024) Vol. 80, pp. 102514-102514
Open Access | Times Cited: 40
A review of machine learning for modeling air quality: Overlooked but important issues
Dié Tang, Yu Zhan, Fumo Yang
Atmospheric Research (2024) Vol. 300, pp. 107261-107261
Closed Access | Times Cited: 39
Size effect on compressive strength of foamed concrete: Experimental and numerical studies
Nengdong Jiang, Zhi Ge, Zhiyuan Wang, et al.
Materials & Design (2024) Vol. 240, pp. 112841-112841
Open Access | Times Cited: 36
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, et al.
Earth s Future (2024) Vol. 12, Iss. 7
Open Access | Times Cited: 33
Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models
Mi Luo, Shoaib Ahmad Anees, Qiuyan Huang, et al.
Forests (2024) Vol. 15, Iss. 6, pp. 975-975
Open Access | Times Cited: 30
Corn Grain Yield Prediction Using UAV-Based High Spatiotemporal Resolution Imagery, Machine Learning, and Spatial Cross-Validation
Patrick Killeen, Iluju Kiringa, Tet Yeap, et al.
Remote Sensing (2024) Vol. 16, Iss. 4, pp. 683-683
Open Access | Times Cited: 25
Estimating the hydrogen adsorption in depleted shale gas reservoirs for kerogens in underground hydrogen storage using machine learning algorithms
Grant Charles Mwakipunda, Mouigni Baraka Nafouanti, AL-Wesabi Ibrahim, et al.
Fuel (2025) Vol. 388, pp. 134534-134534
Closed Access | Times Cited: 5
Assessing the impact of walkability indicators on health outcomes using machine learning algorithms: A case study of Michigan
Musab Wedyan, Fatemeh Saeidi-Rizi
Travel Behaviour and Society (2025) Vol. 39, pp. 100983-100983
Closed Access | Times Cited: 2
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