Geoscience Reference
In-Depth Information
10.8.3
The Importance of Open Data Policies
Furthermore, this survey exhibits the importance of open data policies. This is,
mainly, due to the fact that the extensive recent research activity in regional scales
has been boosted by the currently increasing US and EU open data policies and
mostly by the opening of the United States Geological Survey's Landsat data
archive (Woodcock et al.
2008
; Wulder et al.
2012
) including current and future
missions. Even not in a raw or quality-controlled format and not in a formal
open data framework, there is an increasing availability of Google Earth/Street
View, Microsoft Bing Maps/Streetside data which can also ease certain applications
and studies. All these open data and open source (regarding software) initiatives
and polices ensure the availability of big geospatial data and the availability of
remote sensing datasets spanning densely over longer periods which, moreover, can
enable further research towards quantifying global and regional transitions given the
changing state of the urban environment, global and regional climate, biodiversity,
food, and other critical environmental/ecosystem issues.
10.8.4
The Importance of Automation
The aforementioned availability of open big geospatial data impose as never before
the need for automation. Despite the important advances and the available image
processing technologies, powered mainly from the computer vision community,
still, the skills and experience of an analyst are very important for the success
of a classification/post-classification procedure (Weng
2011
;Luetal.
2011c
),
requiring human intervention which is labor consuming and subjective. Therefore,
introducing generic, automated computational methods in every change detection
component is of fundamental importance.
10.8.5
The Importance of Innovative Basic Research in the
Core of the Change Detection Mechanism
Recent state-of-the-art change detection, classification, and modeling methodolo-
gies are not reaching high (>80 %) levels of accuracy and success rates when
complex and/or extensive regions and/or local scales and/or relative small urban
objects and/or dense time series have been explored in the urban environment
(Wilkinson
2005
; Longbotham et al.
2012
;Bergeretal.
2013
; Rottensteiner et al.
2013
). Thus, there is a strong need for designing new core classification, change
detection, and modeling approaches being able to properly handle the high amount
of spatial, spectral, and temporal information from the new generation sensors,
being able to search effectively through huge archives of remote sensing datasets.
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