globalchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2017.09.010
Scopus记录号: 2-s2.0-85032198349
论文题名:
Automatic extraction of road features in urban environments using dense ALS data
作者: Soilán M; , Truong-Hong L; , Riveiro B; , Laefer D
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2018
卷: 64
起始页码: 226
结束页码: 236
语种: 英语
英文关键词: Airborne laser scanning ; Pavements classification ; Point cloud segmentation ; Urban modelling
Scopus关键词: airborne sensing ; classification ; data processing ; modeling ; parameterization ; pavement ; road ; urban area ; County Dublin ; Dublin [County Dublin] ; Ireland ; Leinster
英文摘要: This paper describes a methodology that automatically extracts semantic information from urban ALS data for urban parameterization and road network definition. First, building façades are segmented from the ground surface by combining knowledge-based information with both voxel and raster data. Next, heuristic rules and unsupervised learning are applied to the ground surface data to distinguish sidewalk and pavement points as a means for curb detection. Then radiometric information was employed for road marking extraction. Using high-density ALS data from Dublin, Ireland, this fully automatic workflow was able to generate a F-score close to 95% for pavement and sidewalk identification with a resolution of 20 cm and better than 80% for road marking detection. © 2017 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79897
Appears in Collections:气候变化事实与影响

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作者单位: Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Spain; Urban Modelling Group, School of Civil, Structural and Environment Engineering, University College Dublin, Belfield, Dublin 4, Ireland; Urban Modeling Group, Center for Urban Science and Progress, New York University, Brooklyn, New York, United States

Recommended Citation:
Soilán M,, Truong-Hong L,, Riveiro B,et al. Automatic extraction of road features in urban environments using dense ALS data[J]. International Journal of Applied Earth Observation and Geoinformation,2018-01-01,64
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