Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5755432 | International Journal of Applied Earth Observation and Geoinformation | 2018 | 11 Pages |
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 20Â cm and better than 80% for road marking detection.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Mario Soilán, Linh Truong-Hong, Belén Riveiro, Debra Laefer,