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

Content Provider	IEEE Xplore Digital Library
Author	Graesser, J. Cheriyadat, A. Vatsavai, R.R. Chandola, V. Long, J. Bright, E.
Copyright Year	2008
Abstract	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.
Starting Page	1164
Ending Page	1176
Page Count	13
File Size	3860802
File Format	PDF
ISSN	19391404
Volume Number	5
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cities and towns Feature extraction Buildings Remote sensing Histograms Spatial resolution Urban areas urban Formal high-resolution image features informal
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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