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.
Highlights
Building detection is the subject of many studies in the field of Remote Sensing due to the wide range of applications that become available thanks to the generated data
A pair of aptical and synthetic aperture radar (SAR) images have been used by Tupin et al (2003) to automatically extract building outlines
Using the previously described method, the number of residential buildings (Table 2), the areas of residential buildings and their volumes have been calculated for each municipality (Table 3)
Summary
Building detection is the subject of many studies in the field of Remote Sensing due to the wide range of applications that become available thanks to the generated data. Koç et al (2005) who developed an approach using high resolution images, by applying classification, and using the digital surface model (DSM) and object extraction techniques. An automated approach has been adopted by Guo et al (2002) who used high resolution IKONOS satellite images and adopted a snake-based approach for 2D building outlines’ extraction from, airborne laser scanning system has been used to capture height data. A pair of aptical and synthetic aperture radar (SAR) images have been used by Tupin et al (2003) to automatically extract building outlines. They first used SAR image to extract partially potential building footprints, they used the optical one to detect shapes based the extracted lines
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