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Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 卷号: 58, 期号: 4, 页码: 2644-2658
Authors:  Jin, Shichao;  Su, Yanjun;  Gao, Shang;  Wu, Fangfang;  Ma, Qin;  Xu, Kexin;  Hu, Tianyu;  Liu, Jin;  Pang, Shuxin;  Guan, Hongcan;  Zhang, Jing;  Guo, Qinghua
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Classification  deep learning  LiDAR  phenotype  segmentation  structural components  
Retrieval of tree branch architecture attributes from terrestrial laser scan data using a Laplacian algorithm 期刊论文
AGRICULTURAL AND FOREST METEOROLOGY, 2020, 卷号: 284
Authors:  Li, Yumei;  Su, Yanjun;  Zhao, Xiaoxia;  Yang, Mohan;  Hu, Tianyu;  Zhang, Jing;  Liu, Jin;  Liu, Min;  Guo, Qinghua
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Trunk architecture  Laplacian algorithm  Dijkstra algorithm  Terrestrial laser scan data  Simulated data  
An updated Vegetation Map of China (1:1000000) 期刊论文
SCIENCE BULLETIN, 2020, 卷号: 65, 期号: 13, 页码: 1125-1136
Authors:  Su, Yanjun;  Guo, Qinghua;  Hu, Tianyu;  Guan, Hongcan;  Jin, Shichao;  An, Shazhou;  Chen, Xuelin;  Guo, Ke;  Hao, Zhanqing;  Hu, Yuanman;  Huang, Yongmei;  Jiang, Mingxi;  Li, Jiaxiang;  Li, Zhenji;  Li, Xiankun;  Li, Xiaowei;  Liang, Cunzhu;  Liu, Renlin;  Liu, Qing;  Ni, Hongwei;  Peng, Shaolin;  Shen, Zehao;  Tang, Zhiyao;  Tian, Xingjun;  Wang, Xihua;  Wang, Renqing;  Xie, Zongqiang;  Xie, Yingzhong;  Xu, Xiaoniu;  Yang, Xiaobo;  Yang, Yongchuan;  Yu, Lifei;  Yue, Ming;  Zhang, Feng;  Ma, Keping
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ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning 期刊论文
IEEE ACCESS, 2020, 卷号: 8, 页码: 148744-148756
Authors:  Hou, Yemao;  Cui, Xindong;  Canul-Ku, Mario;  Jin, Shichao;  Hasimoto-Beltran, Rogelio;  Guo, Qinghua;  Zhu, Min
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Three-dimensional displays  Solid modeling  Computational modeling  Machine learning  Two dimensional displays  Biological system modeling  Shape  Archives of digital morphology  data preprocessing  feature extraction  3D microfossil model classification  deep learning  
Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 10
Authors:  Hu, Tianyu;  Zhang, YingYing;  Su, Yanjun;  Zheng, Yi;  Lin, Guanghui;  Guo, Qinghua
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mangrove  LiDAR  random forest  GLAS  aboveground biomass  
Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level 期刊论文
PLANT METHODS, 2020, 卷号: 16, 期号: 1
Authors:  Jin, Shichao;  Su, Yanjun;  Song, Shilin;  Xu, Kexin;  Hu, Tianyu;  Yang, Qiuli;  Wu, Fangfang;  Xu, Guangcai;  Ma, Qin;  Guan, Hongcan;  Pang, Shuxin;  Li, Yumei;  Guo, Qinghua
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Biomass  Phenotype  Machine learning  Terrestrial lidar  Precision agriculture  
A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2020, 卷号: 13, 页码: 3958-3974
Authors:  Jin, Shichao;  Sun, Yanjun;  Zhao, Xiaoqian;  Hu, Tianyu;  Guo, Qinghua
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Digital terrain model (DTM)  deep learning  fully convolutional neural network (FCN)  ground filtering  light detection and ranging (LiDAR)  
A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments 期刊论文
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 卷号: 166, 页码: 82-94
Authors:  Guan, Hongcan;  Su, Yanjun;  Sun, Xiliang;  Xu, Guangcai;  Li, Wenkai;  Ma, Qin;  Wu, Xiaoyong;  Wu, Jin;  Liu, Lingli;  Guo, Qinghua
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Terrestrial laser scanning  Registration  Marker-free  Forest  
Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data 期刊论文
ECOLOGICAL INDICATORS, 2020, 卷号: 108
Authors:  Xu, Kexin;  Su, Yanjun;  Liu, Jin;  Hu, Tianyu;  Jin, Shichao;  Ma, Qin;  Zhai, Qiuping;  Wang, Rui;  Zhang, Jing;  Li, Yumei;  Liu, Hon An;  Guo, Qinghua
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Aboveground biomass (AGB)  Degraded grassland  Machine learning  Northern agro-pastoral ecotone  Terrestrial laser scanning (TLS)  
Application of deep learning in ecological resource research: Theories, methods, and challenges 期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2020, 卷号: 63, 期号: 10, 页码: 1457-1474
Authors:  Guo, Qinghua;  Jin, Shichao;  Li, Min;  Yang, Qiuli;  Xu, Kexin;  Ju, Yuanzhen;  Zhang, Jing;  Xuan, Jing;  Liu, Jin;  Su, Yanjun;  Xu, Qiang;  Liu, Yu
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Ecological resources  Deep learning  Neural network  Big data  Theory and tools  Application and challenge