Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorYao, W-
dc.creatorWu, J-
dc.identifier.isbn978-981-15-8982-9 (Print ISBN)-
dc.identifier.isbn978-981-15-8983-6 (Online ISBN)-
dc.rights© The Author(s) 2021. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.en_US
dc.rightsThe following publication Yao W., Wu J. (2021) Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics. In: Shi W., Goodchild M.F., Batty M., Kwan MP., Zhang A. (eds) Urban Informatics. The Urban Book Series. Springer, Singapore is available at
dc.titleAirborne LiDAR for detection and characterization of urban objects and traffic dynamicsen_US
dc.typeBook Chapteren_US
dcterms.abstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn Shi W., Goodchild M.F., Batty M., Kwan MP., Zhang A. (Eds. ), Urban Informatics, p. 367-400. Singapore: Springer, 2021-
dc.relation.ispartofbookUrban Informatics-
dc.description.validate202109 bcvc-
dc.description.oaVersion of Recorden_US
Appears in Collections:Book Chapter
Files in This Item:
File Description SizeFormat 
Yao-Wu2021_Chapter_AirborneLiDARForDetectionAndCh.pdf1.3 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

Citations as of Aug 14, 2022


Citations as of Aug 14, 2022


Citations as of Aug 11, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.