TY - GEN
T1 - Automated extraction of building geometry from mobile laser scanning data collected in residential environments
AU - Kashani, Alireza G.
AU - Graettinger, Andrew J.
AU - Grau, David
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Spatial data collection in urban environments for the extraction of building inventory information is important for many applications, such as urban planning, storm water management, hazard mitigation, vulnerability assessment, and loss estimation, to name a few. Creating and updating building inventory databases in large and developing urban environments benefits from efficient data acquisition and data processing techniques. This study leveraged and integrated the advantages of ground-based mobile laser scanning and aerial photography through an automated method to extract building inventory information. The integration of terrestrial and aerial data enables the identification of buildings in the data set and the extraction of both roof polygons and wall footprints. The presented method was evaluated with actual data sets collected from a typical residential area. The area of roof polygons extracted by the automated approach differs by less than 5% when compared to manually calculated values. Also, the proposed method allows for accurate extraction of walls captured within the point cloud.
AB - Spatial data collection in urban environments for the extraction of building inventory information is important for many applications, such as urban planning, storm water management, hazard mitigation, vulnerability assessment, and loss estimation, to name a few. Creating and updating building inventory databases in large and developing urban environments benefits from efficient data acquisition and data processing techniques. This study leveraged and integrated the advantages of ground-based mobile laser scanning and aerial photography through an automated method to extract building inventory information. The integration of terrestrial and aerial data enables the identification of buildings in the data set and the extraction of both roof polygons and wall footprints. The presented method was evaluated with actual data sets collected from a typical residential area. The area of roof polygons extracted by the automated approach differs by less than 5% when compared to manually calculated values. Also, the proposed method allows for accurate extraction of walls captured within the point cloud.
UR - http://www.scopus.com/inward/record.url?scp=84904625864&partnerID=8YFLogxK
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U2 - 10.1061/9780784413517.0098
DO - 10.1061/9780784413517.0098
M3 - Conference contribution
AN - SCOPUS:84904625864
SN - 9780784413517
T3 - Construction Research Congress 2014: Construction in a Global Network - Proceedings of the 2014 Construction Research Congress
SP - 955
EP - 963
BT - Construction Research Congress 2014
PB - American Society of Civil Engineers (ASCE)
T2 - 2014 Construction Research Congress: Construction in a Global Network, CRC 2014
Y2 - 19 May 2014 through 21 May 2014
ER -