Detection of Residential Buildings to Estimate Population in Lebanon using GeoEye Images

Kamel Allaw, Jocelyne Adjizian Gerard, Makram Chehayeb, Nada Badaro Saliba, (doi: 10.23953/cloud.ijarsg.419)

Abstract


Scholars in urban planning and Geography are increasingly interested in grasping demographic information using Remote Sensing data. The accurate detection of residential buildings from satellite images seems to be essential in this domain. This paper has a dual purpose: It aims firstly at developing an automatized method for residential buildings extraction, then, evaluating the relationship between residential building characteristics (number, area, and volume) and demographic data. To do so, a dual phasic methodology is proposed. During the first phase, the extraction of residential buildings has been done using a transformation into HSI representation where the buildings corresponds to the higher values of band I. After that, the image has been transformed into vector and the forms of the buildings have been adjusted using convex hull tool in ArcGIS. The identification of residential buildings has been done using statistical data. The volumes of buildings has been calculated using MATLAB script. During the second phase, a multivariate regression has been established and a strong relationship (R2 =0.87) has been found between the volume of buildings and the population data.

Keywords


Building detection; Convex hull; High resolution satellite image; HIS; Population; Supervised classification

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*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)