Abstract
The high rate of global urbanization has resulted in a
rapid increase in informal settlements, which can be deÞned as unplanned,
unauthorized, and/or unstructured housing. Techniques
for efÞciently mapping these settlement boundaries can beneÞt
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 classiÞcation 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.
