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16*16 pixels as urban or nonurban. The results of the study gave 85.3% accuracy<br />
in terms of correctly identifying blocks as urban (Zhong & Wang, P.3986). The<br />
overall methodology is probably best suited to a larger study area, however it may<br />
be possible to apply the multiple conditional random fields model to a smaller<br />
scale with success.<br />
A further example of similar methodology being applied to aerial data on a large<br />
scale is the 2009 study of the Guangzhou urban area by Fenglei Fan, Yunpeng<br />
Wang, Maohui Qiu and Zhishi Wang. Although similar results to what the authors<br />
achieved in their much larger study would be an effective failure of this thesis the<br />
study indicates that it is possible to determine a lot through automatic image<br />
analysis, even with the disadvantage of poor imagery, random settlement patterns<br />
and a large test area. In the study the authors set out to examine urban growth as<br />
experienced by the people of Guangzhou (a city of 7.5 million inhabitants in the<br />
southern Chinese Guangdong province). They were limited by available imagery –<br />
their study attempted to extract urban areas from a series of images dating back to<br />
the 1970’s and some cycles were not available. They determined that fractal<br />
geometry was useful in studying the development of the city and that a “fractal<br />
dimension index is an effective index to evaluate urban form” (Fan et al, 2009).<br />
The study area covered 3178sqkm and took five separate years as sample points in<br />
time to identify a pattern in the city’s development.<br />
The data capture was completed using a maximum likelihood algorithm<br />
performed on the images. The algorithm took in seven categories to classify the<br />
imagery with; the target urban settlement, forestry, cropland, orchard, natural<br />
water, artificial water and bare land (vegetation free surface area outside the urban<br />
settlement). In order to verify the accuracy of this classification the authors took<br />
reference data captured from fieldwork and separate land use mapping and<br />
sampled the results of their study against each category in the reference. They<br />
achieved an accuracy in correct classification of over 80% using this method. The<br />
study completed segmentation of the imagery by using two transects, running<br />
from west to east, comprising nine blocks of 1306130pixels and south-west to<br />
north-east, comprising ten blocks of the same quantity of pixels.<br />
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