Aerial laser scanning and imagery data fusion for road detection in city scale
|Title:||Aerial laser scanning and imagery data fusion for road detection in city scale||Authors:||Vo, Anh-Vu
Laefer, Debra F.
|Permanent link:||http://hdl.handle.net/10197/7445||Date:||31-Jul-2015||Abstract:||This paper presents a workflow including a novel algorithm for road detection from dense LiDAR fused with high-resolution aerial imagery data. Using a supervised machine learning approach point clouds are firstly classified into one of three groups: building, ground, or unassigned. Ground points are further processed by a novel algorithm to extract a road network. The algorithm exploits the high variance of slope and height of the point data in the direction orthogonal to the road boundaries. Applying the proposed approach on a 40 million point dataset successfully extracted a complex road network with an F-measure of 76.9%.||Funding Details:||European Research Council||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2015 IEEE||Keywords:||Aerial laser scanning; Aerial imagery; Data fusion; Road detection; Machine learning; Hybrid indexing||DOI:||10.1109/IGARSS.2015.7326746||Language:||en||Status of Item:||Not peer reviewed||Conference Details:||2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26 - 31 July 2015|
|Appears in Collections:||Earth Institute Research Collection|
Civil Engineering Research Collection
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