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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:

Supervised and unsupervised machine learning approaches using Sentinel data for flood mapping and damage assessment in Mozambique
M. Nhangumbe, Andrea Nascetti, Stefanos Georganos, et al.
Remote Sensing Applications Society and Environment (2023) Vol. 32, pp. 101015-101015
Open Access | Times Cited: 14

Showing 14 citing articles:

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique
Polina Lemenkova
Coasts (2024) Vol. 4, Iss. 1, pp. 127-149
Open Access | Times Cited: 9
Development of a spatial framework for flash flood damage assessment and mitigation by coupling analytics of machine learning and household level survey data – A case study of rapid collaborative assessments and disbursement of public funds to the affectees of floods 2022, Punjab Pakistan
Urooj Saeed, Mubashar Hussain, Hameedullah, et al.
International Journal of Disaster Risk Reduction (2024) Vol. 108, pp. 104463-104463
Closed Access | Times Cited: 4
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
İrem Üstek, Miguel Arana‐Catania, Alexander Farr, et al.
Earth and Space Science (2024) Vol. 11, Iss. 11
Open Access | Times Cited: 4
Deep learning for rapid crop damage assessment after cyclones
Shiv Kumar
Natural Hazards (2025)
Closed Access
Assessing Machine Learning Models on Temporal and Multi‐Sensor Data for Mapping Flooded Areas
Rogério Galante Negri, Fernando B. Da Costa, Bruna Ferreira, et al.
Transactions in GIS (2025) Vol. 29, Iss. 2
Closed Access
Mapping groundwater-related flooding in urban coastal regions
Montana Marshall, Emmanuel Dubois, Saleck Moulaye Ahmed Cherif, et al.
Journal of Hydrology (2025), pp. 132907-132907
Closed Access
High-Resolution Flood Susceptibility Mapping and Exposure Assessment in Pakistan: An Integrated Artificial Intelligence, Machine Learning and Geospatial Framework
Mirza Waleed, Muhammad Sajjad
International Journal of Disaster Risk Reduction (2025), pp. 105442-105442
Open Access
Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy
George P. Petropoulos, Athina Georgiadi, Kleomenis Kalogeropoulos
GeoHazards (2024) Vol. 5, Iss. 2, pp. 485-503
Open Access | Times Cited: 2
UAV-based DEM augmentation using ConSinGAN for efficient flood parameter prediction with machine learning and 1D hydrodynamic models
Mrunalini Rana, Dhruvesh Patel, Vinay Vakharia, et al.
Physics and Chemistry of the Earth Parts A/B/C (2024) Vol. 135, pp. 103675-103675
Closed Access | Times Cited: 2
Natural Hazards and Climate Change Impacts on Food Security and Rural–Urban Livelihoods in Mozambique—A Bibliometric Analysis and Framework
Alexander Fekete
Earth (2024) Vol. 5, Iss. 4, pp. 761-783
Open Access | Times Cited: 1
Detection of flood-affected areas using multitemporal remote sensing data: a machine learning approach
Robert Kurniawan, Imam Sujono, Wahyu Caesarendra, et al.
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India
Swadhina Koley, Soora Naresh Kumar
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 6
Closed Access
A state-of-the-art review on the quantitative and qualitative assessment of water resources using google earth engine
Rimsha Hasan, Aditya Kapoor, Rajneesh Kumar Singh, et al.
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 12
Closed Access
Comparing machine learning algorithms for predicting and digitally mapping surface soil available phosphorous: a case study from southwestern Iran
Saeid Hojati, Asim Biswas, Mojtaba Norouzi Masir
Precision Agriculture (2023) Vol. 25, Iss. 2, pp. 914-939
Closed Access | Times Cited: 1
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