This document summarizes research on automated damage assessment from satellite imagery after disasters. It presents a novel algorithm to extract buildings from high-resolution satellite images that can detect rooftops of various shapes and colors. The algorithm is applied to images of the National Palace in Haiti before and after the 2010 earthquake. Change detection measures like color histograms and edge density are then used to analyze damage, showing potential to discriminate between collapsed, partially damaged and undamaged buildings. The results demonstrate that this automated approach could provide accurate damage assessments after disasters.

1. Automated Damage Assessment
g
In the Haiti Earthquake
Using Satellite Imagery
Using Satellite Imagery
Jim Thomas1, Ahsan Kareem2, Kevin W. Bowyer3
1
Computer Science and Engineering, Univ. of Notre Dame, IN, USA, jthoma14@cse.nd.edu
2
Civil Engineering, Univ. of Notre Dame, Notre Dame, IN, USA, kareem@nd.edu
3
Computer Science and Engineering, Univ. of Notre Dame, IN, USA, kwb@cse.nd.edu
http://www.nd.edu/~nathaz/
http://nathazlab.blogspot.com/
NatHaz Modeling L b
N H M d li Laboratory
University of Notre Dame
156 Fitzpatrick Hall
Notre Dame, IN 46556
www.nd.edu/~nathaz GCOE Partner
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2. Introduction
 Automated damage detection from satellite or aerial
imagery for post disaster analysis has been a major
research effort in the past decade.
 Previous attempts mostly focused on pixel-based
change detection (figure 1). A damage estimate is
prepared using change detection on before-and-
after event images.
 Such low-resolution analysis limits accuracy to a
rough approximation of estimated damage.
Figure 1
 Th availability of hi h resolution i
The il bilit f high l ti imagery provides an
id
opportunity for a more detailed analysis.
 However, building extraction in this approach is limited
by color/shape variations in roof structures (figure 2).
The
Th ongoing research at U i
i h t University of N t D
it f Notre Dame
focuses on detecting rooftops of all shapes and colors.
 This approach is geared towards the development of
automated, accurate damage assessment techniques
based individual buildings.
b d on i di id l b ildi
Figure 2
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3. Building Extraction
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 A novel algorithm for building detection has been developed.
 This method makes use of supervised/unsupervised
classification for initial segmentation.
 Figure 3 shows a pre-hurricane image from Pensacola, Florida.
Buildings hidden by trees and the variations in roof structure
make this a challenging example for building detection.
Figure 3
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 First step is a segmentation that classifies various
pixels of the image into shadows, buildings, roads,
vegetation etc.; See Figure 4.
 Different combinations of the classified segments are
considered and a hypothesis tests whether the
combination represents a building.
Figure 4
 This hypothesis makes use of shape, spectral and shadow
evidence to accept or reject a group of segments as a building.
 The buildings thus extracted from the above image are marked
in white in Figure 5 and have a detection accuracy of 91%.
g y
Figure 5 3

4. National Palace in Port au Prince, Haiti
Port-au-Prince,
(a) (b)
Figure 6:
g
(a) National
Palace before
the quake;
(b) buildings
extracted from
image in (a).
Figure 7:
(a) National Palace
after the quake;
(b) buildings
extracted from
image in (a).
(a) (b)
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5. Change Detection
 Color-histogram-based change measures were
computed as follows:
 HSV color space histograms were computed
for the before and after images.
 The Bhattacharyya distance d(H1,H2) between
yy ( ,
normalized histograms H1 and H2 is given by:
 This distance represents the change from
before to after the earthquake.
 A false color damage map based on the Lesser More
computed distance is shown in Figure 8. damage damage
Figure 8
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6. Change Detection (
g (Contd.)
)
 Edge density is another measure, that captures non-uniformity of texture resulting
from damage. More edges are likely to appear in an after-event image as a result
damaged or collapsed roofs
d d ll d f
 A difference image of the National palace is shown in Figure 9 and the corresponding
edges detected using the Canny edge detector are shown in Figure 10.
Figure 9 Figure 10
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7. Results
 Plots of damage measures computed vs actual damage states (ground truth) for
color histogram distances (a) and edge density measures (b) are shown below.
 While color-histogram based measures discriminate well between collapsed and
undamaged roofs, edge-density measures are particularly useful in identifying
collapsed rooftops.
1.2 1.2 0.4 0.4
0.35 0.35
1 1
0.3 0.3
0.8 0.8
0.25 0.25
0.6 0.6 0.2 0.2
0.15 0.15
0.4 0.4
0.1 0.1
0.2 0.2
0.05
0 05 0.05
0 05
0 0 0 0
Collapsed Partial Damage No Damage Collapsed Partial Damage No Damage
( )
(a) Color Histogram distances
g ( )
(b) Edge-density measures
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8. Acknowledgment
The support for this study was provided by the Collaborative
Research Program at the NatHaz Laboratory as a part of the
Global Center of Excellence at the Tokyo Polytechnic University:
New Frontier of Education and Research in Wind Engineering. The
funds were provided by the Ministry of Education, Culture, Sports,
Science and Technology (MEXT), Japan. The opinions presented
in this report do not necessarily reflect the views of the
collaborators or the funding agency.
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