H
Hao Xu
Researcher at Nanjing University
Publications - 21
Citations - 517
Hao Xu is an academic researcher from Nanjing University. The author has contributed to research in topics: Point cloud & Lidar. The author has an hindex of 10, co-authored 18 publications receiving 322 citations. Previous affiliations of Hao Xu include China Electronics Technology Group Corporation (China).
Papers
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Registration of Laser Scanning Point Clouds: A Review
TL;DR: A comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing is presented, and the lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods.
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Segmentation of Individual Trees From TLS and MLS Data
TL;DR: A top-down hierarchical segmentation approach, including connectivity-based spatial clustering, stem-based initial segmentation, and fine segmentation of overlapped canopy (canopy scale), is proposed to reduce technical difficulties and improve process efficiency.
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Solar energy potential of urban buildings in 10 cities of China
Liang Cheng,Fangli Zhang,Fangli Zhang,Shuyi Li,Junya Mao,Hao Xu,Hao Xu,Weimin Ju,Weimin Ju,Xiaoqiang Liu,Jie Wu,Kaifu Min,Xuedong Zhang,Manchun Li +13 more
TL;DR: Li et al. as mentioned in this paper estimated the potential of solar energy in cities with a novel roof-facade framework, integrating roofs and facades, considering view shading effects, and correcting weather impacts.
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Enhanced Recent Local Moisture Recycling on the Northwestern Tibetan Plateau Deduced From Ice Core Deuterium Excess Records
Wenling An,Wenling An,Shugui Hou,Shugui Hou,Qiong Zhang,Wangbin Zhang,Shuang-Ye Wu,Shuang-Ye Wu,Hao Xu,Hongxi Pang,Yetang Wang,Yaping Liu +11 more
TL;DR: In this article, local moisture recycling plays an essential role in maintaining an active hydrological cycle of the Tibetan Plateau (TP), and previous studies were largely limited to the seasonal time scale due to sho...
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Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data
TL;DR: The development of a point-based method to effectively solve the problem of objects at different scales, and the establishment of rules to detect changes in buildings and trees to urban areas are established, enabling the use of the point- based method over large areas.