An Integrated Machine Learning Approach for Automatic Highway Extraction from Airborne LiDAR Data and Orthophotos

Pradhan, B; Ibrahim Sameen, M

An Integrated Machine Learning Approach for Automatic Highway Extraction from Airborne LiDAR Data and Orthophotos

Pradhan, B

Ibrahim Sameen, M

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Publisher:: Springer International Publishing
Publication Type:: Chapter
Citation:: Advances in Science, Technology and Innovation, 2020, pp. 61-76
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Ibrahim Sameen, M
dc.date.accessioned	2021-05-05T01:40:27Z
dc.date.available	2021-05-05T01:40:27Z
dc.date.issued	2020-01-01
dc.identifier.citation	Advances in Science, Technology and Innovation, 2020, pp. 61-76
dc.identifier.isbn	9783030103736
dc.identifier.uri	http://hdl.handle.net/10453/148684
dc.description.abstract	Automatic extraction of highways from airborne LiDAR (light detection and ranging) has been a long-standing active research topic in remote sensing (Idrees and Pradhan 2016, 2018;Idrees et al 2016; Fanos et al. 2016, 2018; Fanos and Pradhan 3; Abdulwahid and Pradhan 2017; Pradhan et al. 2016; Sameen et al. 2017; Sameenand Pradhan 2017a, b). Accurate and computationally useful extraction of highway information from remote sensing data is significant for various applications such as traffic accident modeling (Bentaleb et al. 2014), navigation (Kim et al. 2006), intelligent transportation systems (Vaa et al. 2007), and natural hazard assessments (Jebur et al. 2014).
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Advances in Science, Technology and Innovation
dc.relation.isbasedon	10.1007/978-3-030-10374-3_5
dc.rights	info:eu-repo/semantics/openAccess
dc.title	An Integrated Machine Learning Approach for Automatic Highway Extraction from Airborne LiDAR Data and Orthophotos
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-05T01:40:26Z
pubs.publication-status	Published

Abstract:

Automatic extraction of highways from airborne LiDAR (light detection and ranging) has been a long-standing active research topic in remote sensing (Idrees and Pradhan 2016, 2018;Idrees et al 2016; Fanos et al. 2016, 2018; Fanos and Pradhan 3; Abdulwahid and Pradhan 2017; Pradhan et al. 2016; Sameen et al. 2017; Sameenand Pradhan 2017a, b). Accurate and computationally useful extraction of highway information from remote sensing data is significant for various applications such as traffic accident modeling (Bentaleb et al. 2014), navigation (Kim et al. 2006), intelligent transportation systems (Vaa et al. 2007), and natural hazard assessments (Jebur et al. 2014).

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http://hdl.handle.net/10453/148684