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Automatic extraction of road features in urban environments using dense ALS data

Authors: Soilán, Mario
Truong-Hong, Linh
Riveiro, Belén
Laefer, Debra F.
Keywords: Airborne laser scanning;Point cloud segmentation;Urban modelling;Pavements classification;LiDAR;Remote sensing
Issue Date: Feb-2018
Citation: Soilán, M., Truong-Hong, L., Riveiro, B., Laefer, D.F. (2017). Automatic extraction of road features in urban environments using dense ALS data. International Journal of Applied Earth Observation and Geoinformation, 64, 226–236, doi:10.1016/j.jag.2017.09.010
Abstract: 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.
Rights: © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Appears in Collections:Debra Laefer's Collection

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