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International Journal of Digital Earth Volume 17, 2024 - Issue 1
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Research Article

Integrating OpenStreetMap tags for efficient LiDAR point cloud classification using graph neural networks

Yanming Chena School of Earth Sciences and Engineering, Hohai University, Nanjing, People’s Republic of China;b Shenzhen Data Management Center of Planning and Natural Resources, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, People’s Republic of China;c Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, People’s Republic of ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-6075-3683View further author information
ORCID Icon,
Guoli Lia School of Earth Sciences and Engineering, Hohai University, Nanjing, People’s Republic of China;b Shenzhen Data Management Center of Planning and Natural Resources, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, People’s Republic of China;c Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, People’s Republic of ChinaView further author information
,
Xiaoqiang Liud State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaView further author information
,
Yueqian Shena School of Earth Sciences and Engineering, Hohai University, Nanjing, People’s Republic of China;b Shenzhen Data Management Center of Planning and Natural Resources, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, People’s Republic of China;c Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, People’s Republic of ChinaView further author information
,
Jia Lia School of Earth Sciences and Engineering, Hohai University, Nanjing, People’s Republic of China;b Shenzhen Data Management Center of Planning and Natural Resources, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, People’s Republic of China;c Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, People’s Republic of China;e Key Laboratory of Spatial–temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, Shanghai, People’s Republic of ChinaView further author information
&
Qin Tianb Shenzhen Data Management Center of Planning and Natural Resources, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, People’s Republic of ChinaCorrespondence[email protected]
View further author information
Article: 2297946 | Received 10 May 2023, Accepted 18 Dec 2023, Published online: 28 Dec 2023

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