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The Guangzhou study is useful in proving that a relatively high level of accuracy
can be obtained when automatically capturing urban data over a large scale. This
data was improved on by making use of a smaller area for this thesis using higher
resolution imagery and additional indicators (vector and code data imported from
large scale mapping).
This thesis is fortunate not to have the variety in landscape patterns that previous
studies have had to contend with, which meant that a high accuracy level was
possible. In much of the available literature the studies are completed on a very
large scale (as in the previous two papers) with very specific data in mind. They
attempt to identify particular plant species or types of urban development. The
methodology being used for this study may benefit from applying some of those
techniques to a more stable sample. There are several advantages present in the
area being targeted. The temperate nature of the Irish climate means that areas
which are not developed will be covered by vegetation, so may fall into the near
infrared category, while areas under development should display values consistent
with earthworks or paving. At the outset of the study it was expected that most
roofing would fall within a relatively small range of colors and could be used to
calibrate the search. This was not the case, however, tarmac road data proved a
useful replacement in terms of consistent spectral property throughout the image.
The thesis looked at a very specific aspect of this body of knowledge and attempt
to bridge the gap between automatic aerial data capture and traditional
photogrammetric methods. It is noted that in most of the study areas the authors
did not benefit from the availability of large scale coded vector data and the
premise for the study was that if this is available then the accuracy of automatic
capture can be increased. At the core of all of the literature mentioned in this
review is the classification of imagery (with the exception of the point signature
algorithm proposed by Duradev). In the course of this review I encountered one
study which posed one of the same questions that are considered in this thesis; can
the use of geometric information increase classification accuracies in aerial image
processing? This study (Bellens et al, 2008) proposed a method of morphological
profiling to improve the data capture. The authors identified “substantial
improvement” (Bellens et al, P.2803). The study points out that urban areas such
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