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Bailang Yu
Researcher at East China Normal University
Publications - 92
Citations - 5170
Bailang Yu is an academic researcher from East China Normal University. The author has contributed to research in topics: Lidar & Population. The author has an hindex of 30, co-authored 90 publications receiving 3406 citations. Previous affiliations of Bailang Yu include Chinese Ministry of Education & Texas A&M University.
Papers
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Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data
TL;DR: This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.
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Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas
TL;DR: The first global night-time light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day-night band carried by the Suomi National Polar-orbiting Partnership (NPP) satellite were released recently.
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Extracting and understanding urban areas of interest using geotagged photos
TL;DR: A coherent framework for extracting and understanding urban AOI based on geotagged photos, identified using DBSCAN clustering algorithm, understood by extracting distinctive textual tags and preferable photos, and discussed the spatiotemporal dynamics as well as some insights derived from the AOi are presented.
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Automated derivation of urban building density information using airborne LiDAR data and object-based method
TL;DR: It is demonstrated that Building Coverage Ratio, Floor Area Ratio, and other building density indicators can be numerically and automatically derived from high-resolution airborne LiDAR data.
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A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data
Bin Wu,Bailang Yu,Wenhui Yue,Song Shu,Wenqi Tan,Chunling Hu,Yan Huang,Jianping Wu,Hongxing Liu +8 more
TL;DR: The method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data and the completeness and correctness of the method for street tree detection are over 98%.