High Resolution Satellite Images and LiDAR Data for Small-Area Building Extraction and Population Estimation

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Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census ... continued below

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Ramesh, Sathya December 2009.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 998 times , with 5 in the last month . More information about this thesis can be viewed below.

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  • Ramesh, Sathya

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Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.

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  • December 2009

Added to The UNT Digital Library

  • March 17, 2010, 11:40 a.m.

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  • Jan. 14, 2014, 2:01 p.m.

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Ramesh, Sathya. High Resolution Satellite Images and LiDAR Data for Small-Area Building Extraction and Population Estimation, thesis, December 2009; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc12188/: accessed December 15, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .