Automatic extraction of road features in urban environments using dense ALS data
Laefer, Debra F.
|Keywords:||Airborne laser scanning;Point cloud segmentation;Urban modelling;Pavements classification;LiDAR;Remote sensing|
|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 http://creativecommons.org/licenses/by-nc-nd/4.0/|
|Appears in Collections:||Debra Laefer's Collection|
Files in This Item:
|Automatic extraction of road features... ALS data.pdf||Pre-Print||1.28 MB||Adobe PDF||View/Open|
Items in FDA are protected by copyright, with all rights reserved, unless otherwise indicated.