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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:

A convolutional neural network for detecting sea turtles in drone imagery
Patrick Gray, Abram B. Fleishman, David J. Klein, et al.
Methods in Ecology and Evolution (2018) Vol. 10, Iss. 3, pp. 345-355
Open Access | Times Cited: 123

Showing 1-25 of 123 citing articles:

Applications for deep learning in ecology
Sylvain Christin, Éric Hervet, Nicolas Lecomte
Methods in Ecology and Evolution (2019) Vol. 10, Iss. 10, pp. 1632-1644
Open Access | Times Cited: 523
Machine learning and deep learning—A review for ecologists
Maximilian Pichler, Florian Härtig
Methods in Ecology and Evolution (2023) Vol. 14, Iss. 4, pp. 994-1016
Open Access | Times Cited: 200
Deep learning as a tool for ecology and evolution
Marek L. Borowiec, Rebecca B. Dikow, Paul B. Frandsen, et al.
Methods in Ecology and Evolution (2022) Vol. 13, Iss. 8, pp. 1640-1660
Open Access | Times Cited: 164
Uncovering Ecological Patterns with Convolutional Neural Networks
Philip G. Brodrick, Andrew B. Davies, Gregory P. Asner
Trends in Ecology & Evolution (2019) Vol. 34, Iss. 8, pp. 734-745
Closed Access | Times Cited: 149
Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning
Ellen M. Ditria, Sebastian Lopez‐Marcano, Michael Sievers, et al.
Frontiers in Marine Science (2020) Vol. 7
Open Access | Times Cited: 144
Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India
K. Nagaraju Shivaprakash, Niraj Swami, Sagar Mysorekar, et al.
Sustainability (2022) Vol. 14, Iss. 12, pp. 7154-7154
Open Access | Times Cited: 102
Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks
Maximilian Pichler, Virginie Boreux, Alexandra‐Maria Klein, et al.
Methods in Ecology and Evolution (2019) Vol. 11, Iss. 2, pp. 281-293
Open Access | Times Cited: 142
Operational Protocols for the Use of Drones in Marine Animal Research
Vincent Raoult, Andrew P. Colefax, Blake M. Allan, et al.
Drones (2020) Vol. 4, Iss. 4, pp. 64-64
Open Access | Times Cited: 119
Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes
Isla Duporge, Olga Isupova, Steven Reece, et al.
Remote Sensing in Ecology and Conservation (2020) Vol. 7, Iss. 3, pp. 369-381
Open Access | Times Cited: 101
Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry
Patrick Gray, K. C. Bierlich, Sydney A. Mantell, et al.
Methods in Ecology and Evolution (2019) Vol. 10, Iss. 9, pp. 1490-1500
Open Access | Times Cited: 95
Applications, databases and open computer vision research from drone videos and images: a survey
Younes Akbari, Noor Almaadeed, Somaya Al-Máadeed, et al.
Artificial Intelligence Review (2021) Vol. 54, Iss. 5, pp. 3887-3938
Closed Access | Times Cited: 87
Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality
Michael J. Koontz, Andrew M. Latimer, Leif A. Mortenson, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 72
Deep learning for visual recognition and detection of aquatic animals: A review
Juan Li, Wenkai Xu, Limiao Deng, et al.
Reviews in Aquaculture (2022) Vol. 15, Iss. 2, pp. 409-433
Closed Access | Times Cited: 70
Machine learning to detect marine animals in UAV imagery: effect of morphology, spacing, behaviour and habitat
Antoine M. Dujon, Daniel Ierodiaconou, Johanna J. Geeson, et al.
Remote Sensing in Ecology and Conservation (2021) Vol. 7, Iss. 3, pp. 341-354
Open Access | Times Cited: 59
Opportunities and risks in the use of drones for studying animal behaviour
Lukas Schad, Julia Fischer
Methods in Ecology and Evolution (2022) Vol. 14, Iss. 8, pp. 1864-1872
Open Access | Times Cited: 58
The use of drones for mosquito surveillance and control
Gabriel Carrasco-Escobar, Marta Moreno, Kimberly Fornace, et al.
Parasites & Vectors (2022) Vol. 15, Iss. 1
Open Access | Times Cited: 48
An efficient detector for maritime search and rescue object based on unmanned aerial vehicle images
Wanxuan Geng, Junfan Yi, Liang Cheng
Displays (2025), pp. 102994-102994
Closed Access | Times Cited: 1
A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data
Gordana Jakovljević, Miro Govedarica, María Flor Álvarez Taboada
Remote Sensing (2020) Vol. 12, Iss. 9, pp. 1515-1515
Open Access | Times Cited: 71
U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery
Tuan Linh Giang, Kinh Bac Dang, Quang Toan Le, et al.
IEEE Access (2020) Vol. 8, pp. 186257-186273
Open Access | Times Cited: 69
Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam
Kinh Bac Dang, Manh Ha Nguyen, Đức Anh Nguyễn, et al.
Remote Sensing (2020) Vol. 12, Iss. 19, pp. 3270-3270
Open Access | Times Cited: 65
UAV and a deep convolutional neural network for monitoring invasive alien plants in the wild
Wanqiang Qian, Yiqi Huang, Qi Liu, et al.
Computers and Electronics in Agriculture (2020) Vol. 174, pp. 105519-105519
Closed Access | Times Cited: 53
Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
Miguel Martin‐Abadal, Ana Ruiz‐Frau, Hilmar Hinz, et al.
Sensors (2020) Vol. 20, Iss. 6, pp. 1708-1708
Open Access | Times Cited: 51
Determination of optimal flight altitude to minimise acoustic drone disturbance to wildlife using species audiograms
Isla Duporge, Marcus P. Spiegel, Eleanor R. Thomson, et al.
Methods in Ecology and Evolution (2021) Vol. 12, Iss. 11, pp. 2196-2207
Open Access | Times Cited: 50
21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning
Benjamin Kellenberger, Thor Veen, Eelke O. Folmer, et al.
Remote Sensing in Ecology and Conservation (2021) Vol. 7, Iss. 3, pp. 445-460
Open Access | Times Cited: 44
YOLODrone: Improved YOLO Architecture for Object Detection in Drone Images
Oyku Sahin, Sedat Özer
(2021)
Closed Access | Times Cited: 44
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