TY - GEN
T1 - Extraction of buildings footprint from lidar altimetry data with the hermite transform
AU - Silván-Cárdenas, José Luis
AU - Wang, Le
PY - 2011
Y1 - 2011
N2 - Building footprint geometry is a basic layer of information required by government institutions for a number of land management operations and research. LiDAR (light detection and ranging) is a laser-based altimetry measurement instrument that is flown over relatively wide land areas in order to produce digital surface models. Although high spatial resolution LiDAR measurements (of around 1 m horizontally) are suitable to detect aboveground features through elevation discrimination, the automatic extraction of buildings in many cases, such as in residential areas with complex terrain forms, has proved a difficult task. In this study, we developed a method for detecting building footprint from LiDAR altimetry data and tested its performance over four sites located in Austin, TX. Compared to another standard method, the proposed method had comparable accuracy and better efficiency.
AB - Building footprint geometry is a basic layer of information required by government institutions for a number of land management operations and research. LiDAR (light detection and ranging) is a laser-based altimetry measurement instrument that is flown over relatively wide land areas in order to produce digital surface models. Although high spatial resolution LiDAR measurements (of around 1 m horizontally) are suitable to detect aboveground features through elevation discrimination, the automatic extraction of buildings in many cases, such as in residential areas with complex terrain forms, has proved a difficult task. In this study, we developed a method for detecting building footprint from LiDAR altimetry data and tested its performance over four sites located in Austin, TX. Compared to another standard method, the proposed method had comparable accuracy and better efficiency.
KW - Building footprint
KW - Hermite Transform
KW - Local Orientation
UR - https://www.scopus.com/pages/publications/79960122217
U2 - 10.1007/978-3-642-21587-2_34
DO - 10.1007/978-3-642-21587-2_34
M3 - Conference contribution
AN - SCOPUS:79960122217
SN - 9783642215865
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 314
EP - 321
BT - Pattern Recognition - Third Mexican Conference, MCPR 2011, Proceedings
T2 - 3rd Mexican Conference on Pattern Recognition, MCPR 2011
Y2 - 29 June 2011 through 2 July 2011
ER -