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Parcel-Based Change Detection Using Multi-Temporal LiDAR Data in the City of Surrey, British Columbia, Canada

Change detection is amongst the most effective critical examination methods used in remote sensing technology. In this research, new methods are proposed for building and vegetation change detection using only LiDAR data without using any other remotely sensed data. Two LiDAR datasets from 2009 and 2013 will be used in this research. These datasets are provided by the City of Surrey. A Parcel map which shows parcels in the study area will be also used in this research because the objective of this research is detecting changes based on parcels. Different methods are applied to detect changes in buildings and vegetation respectively. Three attributes of object –slope, building volume, and building height are derived and used in this study. Changes in buildings are not only detected but also categorized based on their attributes. In addition, vegetation change detection is performed based on parcels. The output shows parcels with a change of vegetation. Accuracy assessment is done by using measures of completeness, correctness, and quality of extracted regions. Accuracy assessments suggest that building change detection is performed with better results.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc955122
Date12 1900
CreatorsYigit, Aykut
ContributorsDong, Pinliang, Pan, Feifei, Tiwari, Chetan
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
FormatText
RightsPublic, Yigit, Aykut, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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