Case studies on data-rich and data-poor countries

de Alwis Pitts, Dilkushi, Verrucci, Enrica, Vicini, Alessandro and So, Emily (2015) Case studies on data-rich and data-poor countries. Technical Report. University of Cambridge.

SENSUM_D5 03_FINAL_V1.pdf - Whole Document

Item Type:Paper or Report (Technical Report)
Item Status:Live Archive


The aim of Work Package 5 is to assess the needs of decision-makers and end-users involved in
the process of post-disaster recovery and to provide useful guidance, tools and recommendations
for extracting information from the affected area to help with their decisions. This report follows
from Deliverables D5.1 “Comparison of outcomes with end-user needs” and D5.2 “Semi-automated
data extraction” where the team had set out to explore the needs of decision-makers and
suggested protocols for tools to address their information requirements. This report begins with a
summary of findings from the scenario planning game and a review of end-user priorities; it will
then describe the methods of detecting post-disaster recovery evaluation and monitoring attributes
to aid decision making.
The proposed methods in the deliverables D2.6 “Supervised/Unsupervised change detection”
and D5.2 “Semi-automated data extraction” for use in post-disaster recovery evaluation and
monitoring are tested in detail for data-poor and data-rich scenarios. Semi-automated and
automated methods of finding the recovery indicators pertaining to early recovery and monitoring
are discussed.
Step-by-step guidance for an analyst to follow in order to prepare the images and GIS data layers
necessary to execute the semi-automated and automated methods are discussed in section
2. The outputs are presented in detail using case studies in section 3. In order to develop and
assess the proposed detection methods, images from two case studies, namely Van in Turkey and
Muzaffarabad in Pakistan, both recovering from recent earthquakes, have been used to highlight
the differences between data-rich and data-poor countries and hence the constraints on outputs on
the proposed methods.

Keywords:Disaster, Remote sensing, disaster recovery
Subjects:G Mathematical and Computer Sciences > G150 Mathematical Modelling
F Physical Sciences > F832 Remote Sensing
Divisions:College of Science > School of Geography
ID Code:29634
Deposited On:24 Nov 2017 14:13

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