Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape
Abstract
The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be de ned as unplanned, unauthorized, and/or unstructured housing. Techniques for ef ciently mapping these settlement boundaries can bene t 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 classi cation 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.
- Authors:
-
- ORNL
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- Work for Others (WFO)
- OSTI Identifier:
- 1050316
- DOE Contract Number:
- DE-AC05-00OR22725
- Resource Type:
- Journal Article
- Journal Name:
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Additional Journal Information:
- Journal Volume: 5; Journal Issue: 4; Journal ID: ISSN 1939-1404
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; ACCURACY; AFGHANISTAN; BOLIVIA; CATIONS; DECISION MAKING; REMOTE SENSING; SATELLITES; SPATIAL RESOLUTION; STATISTICS; DECISION TREE ANALYSIS
Citation Formats
Graesser, Jordan B, Cheriyadat, Anil M, Vatsavai, Raju, Chandola, Varun, and Bright, Eddie A. Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape. United States: N. p., 2012.
Web. doi:10.1109/JSTARS.2012.2190383.
Graesser, Jordan B, Cheriyadat, Anil M, Vatsavai, Raju, Chandola, Varun, & Bright, Eddie A. Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape. United States. https://doi.org/10.1109/JSTARS.2012.2190383
Graesser, Jordan B, Cheriyadat, Anil M, Vatsavai, Raju, Chandola, Varun, and Bright, Eddie A. 2012.
"Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape". United States. https://doi.org/10.1109/JSTARS.2012.2190383.
@article{osti_1050316,
title = {Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape},
author = {Graesser, Jordan B and Cheriyadat, Anil M and Vatsavai, Raju and Chandola, Varun and Bright, Eddie A},
abstractNote = {The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be de ned as unplanned, unauthorized, and/or unstructured housing. Techniques for ef ciently mapping these settlement boundaries can bene t 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 classi cation 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.},
doi = {10.1109/JSTARS.2012.2190383},
url = {https://www.osti.gov/biblio/1050316},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
issn = {1939-1404},
number = 4,
volume = 5,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2012},
month = {Sun Jan 01 00:00:00 EST 2012}
}