Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape

The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be defined as unplanned, unauthorized, and/or unstructured housing. Techniques for efficiently mapping these settlement boundaries can benefit various decision making bodies. From a remote sensin...

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Bibliographic Details
Published inIEEE journal of selected topics in applied earth observations and remote sensing Vol. 5; no. 4; pp. 1164 - 1176
Main Authors Graesser, Jordan, Cheriyadat, Anil, Vatsavai, Ranga Raju, Chandola, Varun, Long, Jordan, Bright, Eddie
Format Journal Article
LanguageEnglish
Published IEEE 01.08.2012
Subjects
Buildings
Cities and towns
Feature extraction
Formal
high-resolution
Histograms
image features
informal
Remote sensing
Spatial resolution
urban
Urban areas
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Summary:The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be defined as unplanned, unauthorized, and/or unstructured housing. Techniques for efficiently mapping these settlement boundaries can benefit various decision making bodies. From a remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other types of structures (e.g., industrial, commercial, and formal residential). These spatial characteristics are often captured in high spatial resolution satellite imagery. We analyzed the role of spatial, structural, and contextual features (e.g., GLCM, Histogram of Oriented Gradients, Line Support Regions, Lacunarity) for urban neighborhood mapping, and computed several low-level image features at multiple scales to characterize local neighborhoods. The decision parameters to classify formal-, informal-, and non-settlement classes were learned under Decision Trees and a supervised classification framework. Experiments were conducted on high-resolution satellite imagery from the CitySphere collection, and four different cities (i.e., Caracas, Kabul, Kandahar, and La Paz) with varying spatial characteristics were represented. Overall accuracy ranged from 85% in La Paz, Bolivia, to 92% in Kandahar, Afghanistan. While the disparities between formal and informal neighborhoods varied greatly, many of the image statistics tested proved robust.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2012.2190383