Image Processing Reference
In-Depth Information
Implications for Urban Applications of Remote Sensing
The spatial and temporal resolutions of remote sensing data have implications for
its potential usefulness in urban planning and management. Although aerial pho-
tography is still an important remote sensing technology for urban spatial data
acquisition, the very high-resolution sensors are now providing interesting alterna-
tives for many spatial data requirements.
Much research is in progress to improve the ability to extract useful data of urban-
ized areas from these new sensors. The need to deal with the issue of mixed-pixels
(e.g., Ridd 1995 ; Hung and Ridd 2002 ; Chapters 3, 6 and 8 in this volume) in moderate
and even high-resolution images of urban areas remains important. Another problem
frequently encountered in urban environments is related to the fact that the accuracy of
automated image classifications is still smaller than what could be provided by a
human interpreter (Coulter et al., 1999 ). Barnsley and Barr ( 1996 ) also pointed out
that, due to the very complex nature of urban areas, even pixel-based classifications of
very high-resolution images do not necessarily meet the demands for monitoring urban
land use. In fact, the use of higher resolution data can even reduce the accuracy of an
automatic urban land use classification. A variety of classification methods such as
knowledge based systems, artificial neural networks (Yang 2002 ) texture and spatial
metrics (Herold et al. 2003 ) and in particular object-oriented feature extraction (Benz
et al. 2004 ) are under development. However, to date, the highest accuracy for urban
data extraction and classification is generally still the result of visual interpretation.
In addition to such technical considerations related to urban remote sensing it is
worthwhile to also consider the institutional aspects of urban data capture, exchange
and use. The MOLAND project seeks to provide a spatial planning tool that can be
used for assessing, monitoring and modelling the development of urban and regional
environments ( Typically, data on urban areas will be collected
by several organizations and this creates opportunities for the development of Spatial
Data Infrastructures at various spatial levels (Williamson et al. 2003 ), from local to
global. A particularly interesting example of cooperation in Europe is the
INfrastructure for SPatial InfoRmation in Europe (INSPIRE) initiative which was
launched in December 2001 with a view 'to making available relevant, harmonized
and quality geographic information to support formulation, implementation, moni-
toring and evaluation of Community policies with a territorial dimension or impact.'
( INSPIRE is seen as the first step toward a broad
multi sectoral initiative which focuses initially on the spatial information that is
required for environmental policies. A Directive 'establishing an infrastructure for
spatial information in the Community' was approved by the European Parliament
and the Council Of Ministers (Directive 2007/2/EC) in March 2007. As a result of
this legislation all 27 member states are be required to modify existing legislation or
introduce new legislation to implement its provisions by May 2009.
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