Aerial laser scanning and imagery data fusion for road detection in city scale

Anh Vu Vo, Linh Truong-Hong, Debra F. Laefer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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%.

Original languageEnglish (US)
Title of host publication2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4177-4180
Number of pages4
ISBN (Electronic)9781479979295
DOIs
StatePublished - Nov 10 2015
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy
Duration: Jul 26 2015Jul 31 2015

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2015-November

Other

OtherIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
CountryItaly
CityMilan
Period7/26/157/31/15

Keywords

  • aerial imagery
  • aerial laser scanning
  • data fusion
  • hybrid indexing
  • machine learning
  • road detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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