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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:

LWSNet: A Point-Based Segmentation Network for Leaf-Wood Separation of Individual Trees
Tengping Jiang, Qinyu Zhang, Shan Liu, et al.
Forests (2023) Vol. 14, Iss. 7, pp. 1303-1303
Open Access | Times Cited: 11

Showing 11 citing articles:

Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning
Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni, et al.
Remote Sensing of Environment (2024) Vol. 305, pp. 114078-114078
Open Access | Times Cited: 29
Towards a point cloud understanding framework for forest scene semantic segmentation across forest types and sensor platforms
Hao Lu, Bowen Li, Gang Yang, et al.
Remote Sensing of Environment (2025) Vol. 318, pp. 114591-114591
Closed Access | Times Cited: 2
Stem–Leaf segmentation and phenotypic trait extraction of individual plant using a precise and efficient point cloud segmentation network
Jing‐Kun Yan, Fei Tan, Chengkai Li, et al.
Computers and Electronics in Agriculture (2024) Vol. 220, pp. 108839-108839
Open Access | Times Cited: 10
Advancing the measurement of forest vegetation structural traits using terrestrial laser scanning
Akshay Paygude, Hina Pande, Poonam S. Tiwari, et al.
Elsevier eBooks (2025), pp. 179-204
Closed Access
Segmentation of individual trees in urban MLS point clouds using a deep learning framework based on cylindrical convolution network
Tengping Jiang, Shan Liu, Qinyu Zhang, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 123, pp. 103473-103473
Open Access | Times Cited: 9
WLC-Net: A Robust and Fast Deep Learning Wood–Leaf Classification Method
Hanlong Li, Pei Wang, Yuying Wu, et al.
Forests (2025) Vol. 16, Iss. 3, pp. 513-513
Open Access
Dynamic region growing approach for leaf-wood separation of individual trees based on geometric features and growing patterns
Wen Hao, Maoxue Ran
International Journal of Remote Sensing (2024) Vol. 45, Iss. 18, pp. 6787-6813
Closed Access | Times Cited: 1
Extraction of tree branch skeletons from terrestrial LiDAR point clouds
Junyu Gao, Liyu Tang, Honglin Su, et al.
Ecological Informatics (2024), pp. 102960-102960
Open Access | Times Cited: 1
PosE-Enhanced Point Transformer with Local Surface Features (LSF) for Wood–Leaf Separation
Xin Lu, Ruisheng Wang, Huaiqing Zhang, et al.
Forests (2024) Vol. 15, Iss. 12, pp. 2244-2244
Open Access | Times Cited: 1
Wlc-Net: A Robust and Fast Deep-Learning Wood-Leaf Classification Method
李汉隆 Li Hanlong, Pei Wang, Yuhan Wu, et al.
(2024)
Closed Access
Artificial Intelligence and Terrestrial Point Clouds for Forest Monitoring
Maksymilian Kulicki, Carlos Çabo, T. P. Trzcinski, et al.
Current Forestry Reports (2024) Vol. 11, Iss. 1
Open Access
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