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observations, the need for high performance, big geospatial data processing, and
analysis systems, which are able to model and simulate a geospatially enabled
content, is greater than ever. Both in global and local scales, the vision towards
a global human settlement layer (Craglia et al.
2012
) with multiscale volumetric
information describing in detail our planet in 4D (spatial dimensions plus time)
requires generic, automated, efficient, and accurate new technologies.
Towards this end, a significant amount of research is still, nowadays, focusing on
the design, development, and validation of novel computational change detection
procedures. Among them, those concentrating on forest change detection are
holding the biggest share (Hansen and Loveland
2012
) due to the importance on
climate change, biodiversity and the suitability of past and current satellite remote
sensing sensors, their spatial and spectral properties, and operational monitoring
algorithms (Phelps et al.
2013
). Cropland, vegetation, and urban environments are
the other change detection and monitoring targets that benefit more from the current
and upcoming very high-spatial-resolution, very high-spectral-resolution, and very
high-temporal-resolution remote sensing data.
This chapter is focusing on the recent advances on change detection computa-
tional methods for monitoring urban environments from satellite remote sensing
data, with emphasis on the most recent advances in the domain. In order to study
change detection methodologies, their main key components are identified and
studied independently. The most recent techniques are presented in a systematic
fashion. In particular, publications during the last 6 years are reviewed and recent re-
search efforts are classified in certain categories regarding the type of the algorithm
employed, the type of geospatial data used, and the type of the detection target.
Earlier reviews (Lu et al.
2004
,
2011c
; Radke et al.
2005
)giveadetailedsummary
of the efforts during the last decades (Singh
1989
). Moreover, the focus here is on
change detection methods applied to medium-, high-, and very high-resolution data,
since for urban environments smaller scales do not provide spatial products with
suitable accuracies for local geospatial database update. In the following sections
several aspects of the change detection targets, end products, the relevant remote
sensing data, preprocessing, and core change detection algorithms are detailed and
discussed.
10.2
Change Detection Targets and End Products
The main detection targets in urban environments are land cover, land use, urban
growth, impervious surfaces, man-made objects, buildings, and roads. With the
same order one can indicate a suitable spatial accuracy from regional to more
local scales. Therefore, each query for monitoring specific phenomenon, terrain
classes, or terrain object poses specific constrains that describe the end product of
the procedure. Which is the detection target and the desired location and size, which
is the desired time period, and which is the required spatial accuracy?
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