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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
RAPID DISASTER DAMAGE ESTIMATION
TT Vu
? School of Geography, University of Nottingham, Malaysia campus, Jalan Broga, Semenyih, 43500 Selangor,
Malaysia - tuongthuy.vu@nottingham.edu.my
Commission VIII, WG VIII/1
KEY WORDS: Remote Sensing, Feature Extraction, Segmentation, Disaster, Damage mapping
ABSTRACT:
The experiences from recent disaster events showed that detailed information derived from high-resolution satellite images could
accommodate the requirements from damage analysts and disaster management practitioners. Richer information contained in such
high-resolution images, however, increases the complexity of image analysis. As a result, few image analysis solutions can be
practically used under time pressure in the context of post-disaster and emergency responses. To fill the gap in employment of
remote sensing in disaster response, this research develops a rapid high-resolution satellite mapping solution built upon a dual-scale
contextual framework to support damage estimation after a catastrophe. The target objects are building (or building blocks) and their
condition. On the coarse processing level, statistical region merging deployed to group pixels into a number of coarse clusters. Based
on majority rule of vegetation index, water and shadow index, it is possible to eliminate the irrelevant clusters. The remaining
clusters likely consist of building structures and others. On the fine processing level details, within each considering clusters, smaller
objects are formed using morphological analysis. Numerous indicators including spectral, textural and shape indices are computed to
be used in a rule-based object classification. Computation time of raster-based analysis highly depends on the image size or number
of processed pixels in order words. Breaking into 2 level processing helps to reduce the processed number of pixels and the
redundancy of processing irrelevant information. In addition, it allows a data- and tasks- based parallel implementation. The
performance is demonstrated with QuickBird images captured a disaster-affected area of Phanga, Thailand by the 2004 Indian Ocean
tsunami are used for demonstration of the performance. The developed solution will be implemented in different platforms as well as
a web processing service for operational uses.
1. INTRODUCTION automated image analysis solution is available, the practitioners
are seeking the collaborative mapping framework (Goodchild
Providing quick and reliable captured information in hardest hit and Glennon 2010). GEO-CAN (Bevington et al. 2010), a
and difficult-to-assess areas, remote sensing products have been practical work has been efficiently deployed in response to the
commonly used as the first and primary information source at 2010 Haiti earthquake, in which the huge time-consuming
the post-disaster response stage (Adams et al., 2004; Balz and interpretation of damages from remote sensing images were
Liao, 2010; Matsuoka and Yamazaki 1999; Saito et al., 2004; ^ divided into gird-based delegation to numerous contributors
Stramondo et al, 2006; Vu et al. 2005). The activation of worldwide. As collaborative mapping platform is well
International Charter on Space and Major Disasters ^ developed, the mechanism to ensure the accuracy and
(www.disastercharter.org) together with the coordination of ^ consistency is the big challenge. Moreover, damage
UN-SPIDER (www.un-spider.org) facilitates the acquiring and interpretation requires a certain level of expertise in remote
delivering timely remote sensing images to the bodies in charge sensing and structural engineering. The quick image processing
of relief efforts and emergency responses. The fastest available ^^ outcomes would be a guideline for the contributors as well as a
information is damage extent that is manually extracted in such reference frame to ensure the quality of derived damage
operational framework. The product accuracy with respect to information.
the practices is far to meet (Kerle 2010). More quantitative and
details of damages are expected from the damage analysts and To contribute to the current efforts, this research develops a
disaster management practitioners. Researches in response to rapid high-resolution satellite mapping solution built upon a
recent disaster events showed that detailed information derived dual-scale contextual framework to support damage estimation
from very high-resolution satellite images could accommodate after a catastrophe. The initial development is formulated as a
their requirements (Chesnel et al. 2007; Gusella et al. 2005; ^ part of a tsunami-damage estimation system (Koshimura et al.
Saito et al. 2004; Vu and Ban, 2010). 2010) integrating numerical modelling of tsunami inundation,
remote sensing and GIS. High-resolution remote sensing images
Richer information contained in such high-resolution images are acquired to update the surface roughness for tsunami
increases the complexity of image analysis. Numerous modelling as well as mapping the structural damages. Thus, the
researches have been done either to develop or employ the ^ target objects of image processing are building (or building
object-based image analysis approach (Blaschke 2010), which blocks). Breaking into two levels of processing, the design and
proved to be the most suitable for high-resolution satellite ^ implementation are optimized for computation speed. Details of
images. Those, however, are impractically used under time developed solutions are described in Section 2 and
pressure in the context of post-disaster and emergency demonstrated with QuickBird images of Ban Nam Ken, Phanga,
responses due to the high computational cost and the Thailand in Section 3.
requirement of experienced operators. Since no mature
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