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]

https://orcid.org/0000-0002-6075-3683 View further author information

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

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