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面向高精度城市测绘的激光紧耦合SLAM方法 期刊论文
测绘学报, 2021, 卷号: 50, 期号: 11, 页码: 1585-1593
Authors:  孙喜亮;  关宏灿;  苏艳军;  徐光彩;  郭庆华
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同时定位与地图构建  紧耦合  特征提取  点云配准  全局优化  
基于激光雷达的自然资源三维动态监测现状与展望 期刊论文
遥感学报, 2021, 卷号: 25, 期号: 01, 页码: 381-402
Authors:  李玉美;  郭庆华;  万波;  秦宏楠;  王德智;  徐可心;  宋师琳;  孙千惠;  赵晓霞;  杨默含;  吴晓永;  魏邓杰;  胡天宇;  苏艳军
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遥感  激光雷达  自然资源  三维信息  动态监测  数据融合  
中国植被生态学研究的进展和趋势 期刊论文
中国科学:生命科学, 2021, 卷号: 51, 期号: 03, 页码: 215-218
Authors:  马克平;  郭庆华
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植被生态学  植物群落  新一代  植被图  植被制图  
轻小型无人机遥感及其行业应用进展 期刊论文
地理科学进展, 2021, 卷号: 40, 期号: 09, 页码: 1550-1569
Authors:  郭庆华;  胡天宇;  刘瑾;  金时超;  肖青;  杨贵军;  高显连;  许强;  谢品华;  彭炽刚;  闫利
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数据融合  多传感器集成  智能化飞行  无人机组网  深度学习  
“绿途”系统:公民科学时代的植被调查制图新工具 期刊论文
中国科学:生命科学, 2021, 卷号: 51, 期号: 03, 页码: 362-374
Authors:  金时超;  胡天宇;  苏艳军;  马勤;  关宏灿;  杨默含;  郭庆华
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绿途  植被图  众包  公民科学  APP  网页端  
新一代1:50万中国植被图绘制方法探讨 期刊论文
中国科学:生命科学, 2021, 卷号: 51, 期号: 03, 页码: 229-241
Authors:  郭庆华;  关宏灿;  胡天宇;  金时超;  苏艳军;  王雪静;  魏邓杰;  马勤;  孙千惠
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中国植被图  植被制图  卫星遥感  众源采集  深度学习  
An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images 期刊论文
SENSORS, 2020, 卷号: 20, 期号: 22
Authors:  Sun, Fei;  Fang, Fang;  Wang, Run;  Wan, Bo;  Guo, Qinghua;  Li, Hong;  Wu, Xincai
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image classification  class imbalance  impartial semi-supervised learning strategy (ISS)  extreme gradient boosting (XGB)  very-high-resolution (VHR)  
Drought-modulated allometric patterns of trees in semi-arid forests 期刊论文
COMMUNICATIONS BIOLOGY, 2020, 卷号: 3, 期号: 1
Authors:  Dai, Jingyu;  Liu, Hongyan;  Wang, Yongcai;  Guo, Qinghua;  Hu, Tianyu;  Quine, Timothy;  Green, Sophie;  Hartmann, Henrik;  Xu, Chongyang;  Liu, Xu;  Jiang, Zihan
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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  
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