TY - JOUR
T1 - Automatic extraction of road features in urban environments using dense ALS data
AU - Soilán, Mario
AU - Truong-Hong, Linh
AU - Riveiro, Belén
AU - Laefer, Debra
N1 - Funding Information:
This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through the project HERMES:S3D – Healthy and Efficient Routes in Massive Open-data based Smart Cities (Ref.: TIN201346801-C4-4-R), Human Resources program FPI (Grant BES-2014-067736), and Fundación Barrié (Grant holder – Ayudas a la Movilidad Internacional de Jóvenes Investigadores de Programas de Doctorado Sistema Universitario de Galicia 2016).
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Airborne laser scanning
KW - Pavements classification
KW - Point cloud segmentation
KW - Urban modelling
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U2 - 10.1016/j.jag.2017.09.010
DO - 10.1016/j.jag.2017.09.010
M3 - Article
AN - SCOPUS:85032198349
SN - 1569-8432
VL - 64
SP - 226
EP - 236
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
ER -