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APPROVED:
Pinliang Dong, Major Professor Chetan Tiwari, Committee Member Xiaohui Yuan, Committee Member Paul Hudak, Chair of the Department of
Geography Mark Wardell, Dean of the Toulouse Graduate
School
ASSESSMENT OF POST-EARTHQUAKE BUILDING DAMAGE USING HIGH-RESOLUTION SATELLITE
IMAGES AND LiDAR DATA – A CASE STUDY FROM PORT-AU-PRINCE, HAITI
Mehrdad Koohikamali
Thesis Prepared for the Degree of
MASTER OF SCIENCE
UNIVERSITY OF NORTH TEXAS
August 2014
Koohikamali, Mehrdad. Assessment of Post-Earthquake Building Damage Using High-
Resolution Satellite Images and LiDAR Data - A Case Study from Port-au-Prince, Haiti. Master of
Science (Applied Geography), August 2014, 66 pp., 7 tables, 21 illustrations, references, 36
titles.
When an earthquake happens, one of the most important tasks of disaster managers is
to conduct damage assessment; this is mostly done from remotely sensed data. This study
presents a new method for building detection and damage assessment using high-resolution
satellite images and LiDAR data from Port-au-Prince, Haiti. A graph-cut method is used for
building detection due to its advantages compared to traditional methods such as the Hough
transform. Results of two methods are compared to understand how much our proposed
technique is effective. Afterwards, sensitivity analysis is performed to show the effect of image
resolution on the efficiency of our method. Results are in four groups.
First: based on two criteria for sensitivity analysis, completeness and correctness, the
more efficient method is graph-cut, and the final building mask layer is used for damage
assessment. Next, building damage assessment is done using change detection technique from
two images from period of before and after the earthquake. Third, to integrate LiDAR data and
damage assessment, we showed there is a strong relationship between terrain roughness
variables that are calculated using digital surface models. Finally, open street map and
normalized digital surface model are used to detect possible road blockages. Results of
detecting road blockages showed positive values of normalized digital surface model on the
road centerline can represent blockages if we exclude other objects such as cars.
Copyright 2014
by
Mehrdad Koohikamali
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ACKNOWLEDGEMENTS
This thesis was made possible, in part, by my graduate committee’s continuous help and
guidance during this research. I wish to thank my major professor Dr. Pinliang Dong for his
support and patience during this research. I would also like to thank Dr. Chetan Tiwari and Dr.
Xiaohui Yuan for their very helpful suggestions and help. Special thanks to my family and all
those who have provided help for my continuing education. Finally, I would like to dedicate this
thesis to my people in Bam (Iran) who were killed, injured, or survived in the 2003 earthquake.
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TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ......................................................................................................... iii
LIST OF TABLES ...................................................................................................................... vi
LIST OF ILLUSTRATIONS ........................................................................................................ vii
INTRODUCTION ..................................................................................................................... 1
Quick Response after an Earthquake ................................................................................ 1
Building Damage Assessment and Remote Sensing .......................................................... 2
Research Objectives .......................................................................................................... 6
BACKGROUND ....................................................................................................................... 7
Disaster Management and Remote Sensing ..................................................................... 7
Post-Earthquake Building Damage Assessment ................................................................ 9
Building Extraction by Image Segmentation ................................................................... 11
Change Detection ............................................................................................................ 14
Terrain Roughness ........................................................................................................... 15
Sensitivity Analysis........................................................................................................... 15
STUDY AREA, DATA, AND SOFTWARE ................................................................................. 16
Study Area ....................................................................................................................... 16
Data ................................................................................................................................. 17
Software and Tools .......................................................................................................... 21
METHODOLOGY .................................................................................................................. 23
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Data Preprocessing .......................................................................................................... 24
Building Extraction and Damage Assessment ................................................................. 27
Building Damage Assessment .......................................................................................... 33
Terrain Roughness ........................................................................................................... 34
Sensitivity Analysis........................................................................................................... 35
Possible Road Blockages .................................................................................................. 36
RESULTS AND DISCUSSION .................................................................................................. 38
Histogram Matching ........................................................................................................ 38
LiDAR Data Classification and nDSM Generation ............................................................ 39
OSM Reliability Analysis .................................................................................................. 40
Building Extraction ........................................................................................................... 42
Building Damage Assessment .......................................................................................... 46
Terrain Roughness ........................................................................................................... 48
Possible Road Blockages .................................................................................................. 50
CONCLUSION ....................................................................................................................... 52
Limitation and Future Studies ......................................................................................... 53
REFERENCES ........................................................................................................................ 55
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LIST OF TABLES
Page
1 - Previous Studies about Building Extraction ....................................................................... 12
2 – Building Damage Classification Criteria ............................................................................ 33
3 – Terrain Roughness Index Categories ................................................................................. 35
4 - Positional Accuracy of Open Street Map Road Centerline ................................................ 40
5 - Building Extraction Quality Measures with Graph-Cut and Hough Transform ................. 45
6 - Comparison of Building Damage Levels............................................................................. 48
7 - Comparison of Building Damage Levels and Terrain Roughness Variables....................... 50
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LIST OF ILLUSTRATIONS
Page
1 – Study Area: City of Port-au-Prince .................................................................................... 16
2 – GeoEye-1 Images: Before (left) and after (right) Earthquake ........................................... 18
3 - LiDAR Data Over Haitian Palace ......................................................................................... 19
4 – RCL of OSM: Before (left) and After (right) Earthquake ................................................... 20
5 – Damage level map provided by UNOSAT .......................................................................... 20
6 – General Flowchart of the Study ........................................................................................ 24
7 – Flowchart of the Building Damage Assessment ................................................................ 28
8 - An Example of Hough Transform and the Corresponding Edge Point .............................. 30
9 - An Example of Graph Cuts and the Corresponding Vertex Labeling ................................. 31
10 – Flowchart of the Possible Road Blockage Detection ...................................................... 37
11– Histograms of Images at Two Different Times ................................................................ 38
12 – Matched Histogram of Post-Earthquake Image ............................................................. 38
13 – DEM, DSM, and nDSM of City of Port-au-Prince, Haiti, 2010 ......................................... 39
14 –OSM and DLR Road Networks .......................................................................................... 41
15 – Adjusted OSM and DLR Road Networks ......................................................................... 41
16 – Building Extraction Result by the Hough Transform ....................................................... 43
17 – Building Extraction Result by the Graph-Cut .................................................................. 44
18 – Building Damage Assessment ......................................................................................... 47
19 – Standard Deviation of DSM ............................................................................................. 49
20 – TRI of DSM ....................................................................................................................... 49
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21 – Possible Road Blockage........... ........................................................................................ 51
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INTRODUCTION
Quick Response after an Earthquake
Earthquake is one of the most difficult natural disasters to be predicted. Yet, minimizing its
consequences is possible. Generally speaking, the ultimate goal of the disaster management
team is to “provide relief and rescue to affected people” (Hussain et al., 2011, P.1012).
However, insufficient local and updated information about the stricken area stands as major
obstacles. Since methods and programs depend on an unpublished or official data, thus to get
published (Goodchild, 2007). Meanwhile, as for the rescue teams, managers, and decision-
makers—to work efficiently and accomplish the main goal—that in minimizing the earthquake
destruction they must have and need the latest accurate hazard map, to point out quickly the
exact catastrophic areas.
Time of response is a crucial factor in gauging the efficiency of the disaster management.
However, this factor is largely affected by the deficiency of the professionals and trained people
who would have participated in the “map-making” of the hazard map that points out the
destroyed geographical areas, that impedes a good timely relief. By the same token, volunteers
are another important factor and participants that would contribute and accelerate the rescue
and relief by participating in drawing the hazard map by reporting accurate information that
help the emergency management in experimenting and creating new solution (Hussain et al.,
2011). However, a rigorous study to explain how in disaster management we are able to utilize
volunteer geographic information (VGI) as an ancillary provider of information is necessary.
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Catastrophic earthquakes with large magnitudes rupture and affect different aspects of
people’s lives. The consequence of the rupture is divided into two types: financially related
types and physically. While the former could be financially loss, the latter, the physical, has
much more massive influence in the lives of the catastrophe subjects. Immediate actions should
be utilized in order to locate wounded people and provide them with medical help and aid to
minimize the risk of death as much as possible. The term “golden hours” refers to the first 72
hours after the earthquake; these hours are vital in rescuing and helping people who are under
threat. Moreover, chances are very high to rescue and save people’s lives during this time. It is
going without a say that time is the most crucial factor to denigrate an earthquake’s effect and
maximize deliverance (Fiedrich et al., 2000a). Quick preparation of building damage maps, in
minute scale, is vital to enable rescue teams to act immediately and efficiently in saving lives.
Building Damage Assessment and Remote Sensing
Locating the damaged buildings is one a thrift research area that sustains interests. Since its
focus is trapped, injured people and those who seek help. Different assessment methods have
been developed to examine damaged building. Almost all of them have incorporated remote
sensing data to extract information (Hussain et al., 2011). These methods are, mainly, based on
satellite images and very sensitive to the image resolution and time of capturing. However,
detecting finer details is difficult from the low resolution images; furthermore, change
detecting is not feasible if pre-and-post earthquake images do not exit. However, the
development and the launch of high-resolution satellite images such GeoEye and IKONOS have
enhanced the final results .In addition, recent studies have sought to develop new methods to
extract finer details out of images.
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Damage assessment methods have used pre-and-post earthquake satellite images to
identify the changes. In general, damaged building, debris, blocked roads, fallen constructions,
or displaced constructions among other are the common post-earthquake changes. Relatively,
damage assessment for large geographical areas using satellite images is well documented
(Turker and Sumer, 2008). On the other hand, damage mapping of buildings, in a smaller scale,
is still arduous process lack ancillary information. Traditional methods have utilized satellite
images and light detection and ranging (LiDAR) data to indicate damaged levels of building after
earthquake in both large destroyed areas and for a limited number of building too.
However, these methods are not always efficient, since some damaged areas and types are
not detected by satellite images, due to the incapability of satellite images to detect the
elevation changes. Though LiDAR data provide digital surface model (DSM) over the area of
interest; LiDAR is not usually available for pre-earthquake situation; otherwise, it could provide
useful information for analysis as ancillary information. On the ground that none of the remote
sensing data types can solely satisfy rapid mapping requirements, data integration is
unavoidable (Ozisik and Kerle, 2004); neither LiDAR data nor satellite images can independently
provide sufficient information about the various earthquake damages (Ma, 2004).
Up-dated, immediate damage maps after the earthquake are crucial. Most studies have
addressed that the first three days after the earthquake is vital to consolation and relief.
However, determining damaged building in a larger affected area in the golden hour period is
not feasible through the current methods because of:
- Long preparation and analysis time for post-earthquake LiDAR and satellite images
- Shortage in professional and experts who can process and work with the data
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- Inefficient developed algorithms for massive number of buildings
Both satellite images and LiDAR are generated and processed by professionals, thus, their
availability in disaster situations require a waiting time. Traditional classifying methods for
damaged buildings relied on pre-and-post earthquake information. Though, automated
methods have been developed for building damage assessment, visual interpretation is still
unavoidable; grounding the fact, that none of them can take into account all parameters such
as shape, geometry, slope and elevation simultaneously (Dong and Guo, 2012).
Furthermore, previous methods have not used distant volunteers, who want to participate
in the rescue and relief aids. Decision makers, rescue teams, and managers need up-to-date
information about the current conditions, level, and type of building damages to provide a
sufficient help to the injured people. Post 2007, when VGI had been defined as the user
generated geographic data, or as another version of crowdsourcing, which is also geo-
referenced, there has been a vast interest in using such treasure for geographical analysis and
mapping (Goodchild, 2007; Goodchild and Li, 2012). Though, the quality of VGI emerges as an
actual impediment to practical utilization in emergency management and disasters, it remains
and has numerous advantages effected on people, as well as, on rescue team (Xu, 2010).
The dearth of available resources regarding the subsequences of big earthquakes,
particularly in undeveloped countries, remains a big problem. The situation becomes more
severe and difficult because of shortage and limited number of professionals who can
contribute and enhance the rescue and relief activities. Equally important is that all rescues and
other effort should be accelerated at the earthquake aftermath. Conversely, previous studies
never practically utilized volunteer’s aids in the post-earthquake’s endeavors.
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Furthermore, in different catastrophic areas in the world, primary maps are not available
upon the time of incident. Researchers in different disciplines, mainly, in geography have tried
to improve different methods for rapid earthquake damage assessment by integrating different
geographic data types. A distinctive factor that voluntarism drastically rises after the
earthquake this study aims to underscore the necessity of incorporating volunteer geographic
information in damage assessment.
The proposed method uses different geographical information such as VGI, LiDAR and
satellite images in an integrated manner for building damage assessment. Incorporation of VGI
satellite images and LiDAR provide varied information for building damage assessment. After
revealing the value of volunteer’s contribution there should be a formal study to incorporate
them and fill out the gap in previous studies (Välimäki, 2011).
This study proposes a method for integrating professional geographic information such as:
high-resolution satellite images and LiDAR with, one of the available sources of VGI (e.g. Open
Street Map (OSM)) to prepare building damage maps and road blockages after the earthquake
in a timely manner. In addition to the added value of VGI, the proposed method in our study
tries to use a new image processing method (graph-cut) for an improved building extraction
results. I also integrated results of graph-cut and LiDAR data together to investigate how a
single LiDAR data can be used effectively in building damage assessment. By the same token,
none of the previous studies in geographic information systems (GIS) have used graph
algorithms for efficient building extraction after disasters. The proposed study uses an image
segmentation method to detect buildings and then classify damage levels by using a change
detection method. In addition, road centerlines from Open Street Map (OSM), is used address
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the road blockages. Because, road centerlines constrain building boundaries (prior shape);
meanwhile, they also can reflect damages and any road blockage or change at any level. Finally,
because in many situations LiDAR data is not available for a period of pre-earthquake, use of
LiDAR data can be misleading. I present terrain roughness variables to understand the
correlation between them as indexes of elevation and the damage level map created by change
detection.
Research Objectives
The research objectives of my study are three folds: (1) to develop an efficient building
extraction method from high resolution satellite image; (2) to evaluate sensitivity of building
extraction method to the resolution of the input images; (3) to test if the post-earthquake
LiDAR data can show any signatures of damaged buildings.
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BACKGROUND
Disaster Management and Remote Sensing
Disaster management consists of four stages: preparedness, mitigation, response and
recovery (Montoya, 2002). The mitigation phase is related to the precaution activities that aim
to reduce the effects of any disaster; while preparedness activities are planned to response and
react efficiently to the occurred disaster. The former two phases have been designed to deal
with the aftermath of the disaster: when people are in most need for help to get them back to
normal live. The fact that current technology does not provide adequate indications or signals
for a precise earthquake conjectures, yet there is a necessity for a more developed research
and information to enhance the mitigation action. However, advanced mitigation is possible
and achievable by remotely sensed information (Liu et al., 2011).
Several researches and studies have utilized remotely sensed information as the primary
source of data for their analysis; its prompt availability after disaster and its wide coverage, all
together, encourage its adoption in the aforementioned researches and studies (Liu et al.,
2011). It is argued that none of the in-situ techniques can perform such a wonderful task with
high accuracy and with a little time consuming. Satellite imagery provides information that
covers a broad area even when instant approachability is not feasible. Here below are some of
the advantages for using satellite images as a data source (Kerle and Oppenheimer, 2002):
- Available in any situation even after catastrophic disasters
- Minimum site work to prepare an accurate map
- Data are almost ready to use and analyze
- Countless samples among the area because of full and continuous coverage
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- Does not require a direct access to the suffered area for data preparation
Launching of high resolution satellite images such as IKONOS and GeoEye help to classify
the damage levels, an effort that was unattainable, if not possible, by using lower resolution
images (Barnes et al., 2007). For example, in the 1999 Marmara earthquake multi-spectral and
panchromatic spot images were used to prepare the damage map. Furthermore, pixel based
change detection techniques were also used to find the changes in a regional scale. However,
the utilization of moderate-resolution of Spot images enables the damage maps to reflect only
the damage levels in a small scale. Traditional methods were labor intensive and they were not
designed for the quick response (Liu et al., 2011).
Therefore, in the case of a big magnitude earthquake, after the earthquake there is a crucial
need for precise damage maps that speculate the actual condition of the earthquake’s location,
despite lack of time and the limitation of the resources. Time pressure and lack of available
resources are two barriers that the decision makers have to face immediately after the
disasters such as a big earthquake (Fiedrich et al., 2000b). At the time when Haiti earthquake
occurred in 2010, the decision-makers and rescue teams had to challenge the lack of precise
and updated data about the situation; up-to-date information about the level of damages in the
catastrophic area’s periphery was a necessity for them that would enhance their performance
in such case.
Simultaneously, they are not merely used remote sensing and LiDAR, but also they tried to
take advantage of the volunteers in the response process (Jobe, 2011). The core motivation for
using VGI is its “inaccessibility and cost of accurate sources” which is ideal on disaster mapping
(Zook et al., 2010, P.5).
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Post-Earthquake Building Damage Assessment
Damage maps are the primary source for the information needed to assist decision makers,
rescue teams, and managers for preparing the short and long term plans and activities (Jobe,
2011). After big magnitude earthquakes, most common physical damage patterns are:
buildings, roads, and infrastructures. Building damage assessment is, by far, the most important
type to be determined due to the fact that buildings constitute the major setting where civilian
casualties, injuries and trapped people need help. However, damaged building would have
constituted the focus of most rescue and relief activities.
The disastrous earthquake in Haiti in 2010 underscores the necessity for pre-disaster maps,
which would help to show the roads for immediate critical assets. Furthermore, the high
demands for online maps emerge and emphasize the importance of crowdsourcing of
information (Zook et al., 2010, P.5). In addition, the growing attention toward a new concept of
Web 2.0 has attracted researcher to its beneficiary effects. This new concept is also known as
peer production, and it is evolved as collaborative activities among people around the world in
project with mutual scope (Graham, 2010). An example of Web 2.0 project is the Open Street
Map (OSM) project; this project makes free street maps around the world, particularly in
developing countries. Haiti earthquake was the first disaster for which volunteers developed a
Web of massive geographic information (Forrest, 2010).
Within the first couple of days post to the earthquake, determining the level and type of
damages in the effected buildings are very important. However, damage classification would
have conducted by interpreting and field surveying that require a high level of accuracy;
furthermore, this procedure is very time consuming, thus, Remote Sensing emerges as the best
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source for up-to-date information (Grebby et al., 2011). Since concentration of people who are
trapped in the buildings would determine congestion of rescue activities damage type
determination is crucial (Rehor and Bähr, 2007). Categorization of damage types is based on
level of damages; five main damage types evolved and could be defined as:
- Inclined layer: the top story is inclined to the wall or to the corner. It consists of three sub-
damage types” “inclined plane,” “multilayer collapse,” and “outspread multilayer collapse”
- Pancake collapse: form and shape of the building are preserved but its height is changed.
The differentiation types of the pancake collapses are based on the part of the building that
had been collapsed
- Debris heaps: when all structural elements of the building are collapsed, this damage is
known as a kind of debris heaps.
- Overturn collapse: in this group either the building has collapsed while the lower part of the
building is still the same, or the upper part lies separately
- Overhanging elements: this category describes damages, whereas the supporting walls are
destroyed, but the building’s roof is intact (Schweier, 2007)
In order to determine type of a building, using Digital Surface Model (DSM) is necessary
which it can be generated by utilizing LiDAR information. Yet, it is an unmanageable to interpret
damaged buildings in the absence of pre-earthquake LiDAR data, as well as, being very time
consuming and labor-intensive, if it is the only source of information (Dong and Guo, 2012).
Previous studies have shown that incorporating additional data sources might enhance the
accuracy and reduce the bias of LiDAR estimates at the same time (Ørka et al., 2010) .
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Building Extraction by Image Segmentation
Image segmentation has emerged as one of the fundamental problems in pattern
recognition. Image segmentation, in general, refers to segmenting an image into different
classes in which each class represents a specific object in the image (Wertheimer, 1938).
Different methods of building extraction have evolved; however snake algorithm, edge
detection, Hough transform, and watershed algorithm are known as the most popular ones.
Some of prior studies about building extraction from satellite images are summarized in table 1.
Many studies have used shadow information to detect buildings while others have used
image segmentation technique to determine buildings via images. Both neural network method
and Bayesian approach method classify image pixels into different classes such as building class.
On the other hand, Wavelet and Hough transform methods transform pixel values to parameter
space to detect edges pixels.
Additionally, Snake algorithm, which is also known as, Active Contours addresses the
problem of outlining object boundaries in noisy images (Guo and Yasuoka, 2002; Kabolizade et
al., 2010). Building detection using snake algorithm consists of three steps:
- Approximation of object regions using active contour models and collecting lines
- Generation of object hypotheses through graph search
- Verification of object hypotheses (Peng et al., 2005; Sun et al., 2003)
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Table 1 - Previous Studies about Building Extraction (San, 2009)
Algorithm Reference
Edge detection – Boundary tracing – Shadow Information (Huertas and Nevatia, 1988)
The line linking and Perceptual grouping (Lin and Nevatia, 1998)
Neural network and Bayesian approach (Kim and Nevatia, 1999)
Wavelet transform (Bellman and Shortis, 2000)
Snake model (Fazan and Dal Poz, 2013; Peng and Liu, 2005)
Hough transform (Aggarwal and Karl, 2006; Yen and Chenb, 2013)
Bayesian networks (Pal and Mather, 2005)
Bayesian Markov random field (Eches et al., 2013; Katartzis and Sahli, 2008)
Many studies in GIS have used Hough transform method for building extraction because it
has proved it is an efficient method for detecting rectangular shapes (Tarsha-Kurdi et al., 2007;
Wang and Liu, 2005; Wei et al., 2004). Although low-level vision techniques such as Hough
transform have an advantage of simple implementation and quick response, they are limited
due to the methodological restrictions (Benarchid et al., 2013). In this study I implement a new
method for building extraction to overcome some of the limitation in the Hough transform.
Hough Transform Method
Hough transform is widely used to extract edges and boundaries features of an image. This
method transforms edges into vectors in a parameter space and then edges are connected to
form boundaries (San and Turker, 2010). In this method, curves are detected by exploiting the
duality between points in image and parameters of that curve. Hough transform method
transforms edges of a shape into the accumulator parameter space to delineate the features
boundaries by selecting local maxima (Ballard, 1981). In other words, Hough transform uses
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voting procedure to extract edges and boundaries of features in an image by transforming
edges to vectors in accumulator space and then connecting them to form the boundaries (San
and Turker, 2010).
The efficiency of the Hough transform is dependent on the quality of the input data or high
contrast between boundaries of objects in the image because to correctly detect an object
boundaries there should be high number of votes for it. Also, presence of background noise
degrades the efficiency of this method and correct noise removal is necessary before running
the algorithm (Ballard and Brown, 1992).
Graph-Cut Method
Graph-cut approaches for image segmentation has become one of the leading methods in
image segmentation over the last decade since it allows user interaction, as well as, optimized
global function (Wang et al., 2013). Many reasons stand behind the importance of the graph
theory, however, a particular reasons emerges that of its benefit for solving image
segmentation, that is, no discretization is made by combining operators, while former edge
detection technique such as Canny edge detection, based on abrupt change among adjacent
pixel values (Gonzalez et al., 2009).
The utilization of graph-cuts in image segmentation dated back to 1990 by Wu and Leahy
(Wu and Leahy, 1993). This method is defined as a set of vertices in which individual group
shares similar characteristics, to some extent homogeneous. General graph-cuts are more
globally optimized than other previous image segmentation algorithms. In other different
domains and applications, graph-cuts has a distinctive cost function that could be utilized
(Kleinberg, 2003).
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Change Detection
Change detection methods are based on pre-and-post earthquake satellite images. Change
detection techniques are categorized as either supervised or unsupervised. Upon the
availability or unavailability of real data samples supervised or unsupervised methods can be
implemented. In addition, because real data that are necessary for supervised methods are not
always available in damage assessment for quick response unsupervised methods are preferred
(Bovolo and Bruzzone, 2007; Bruzzone and Prieto, 2000; Celik, 2009; Radke et al., 2005).
Unsupervised methods are two main categories: pixel differentiation and class comparison.
In pixel differentiation methods, damage maps are usually generated using satellite image
differentiation of before and after an earthquake. Image differentiation is basically done by
subtracting pixel values in both images. A threshold is defined to designate changes. For the
class comparison methods, each image is classified and classes are compared to realize possible
changes (Bruzzone and Prieto, 2002; Bustos et al., 2011; Mas, 1999).
In prior studies, different methods are used for creating change maps such as: image
subtraction (Kano et al., 1994), normalized difference vegetation index (Lyon et al., 1998),
change vector analysis (Chen et al., 2003; Malila, 1980), image rationing (Carvalho et al., 2001),
and principle component analysis (PCA) (Byrne et al., 1980; Celik, 2009). PCA is one of the most
used method for multiband satellite images change detection (Bustos et al., 2011). PCA
analyzes the change between two images and it creates eigenvectors. Then, feature vector for
each pixel is calculated and grouped into two clusters by applying k-means algorithm. Finally,
each pixel is assigned to each cluster according to the minimum Euclidean distance that is
between the feature vector and the mean of feature vectors (Celik, 2009).
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Terrain Roughness
Terrain roughness is one of the important surface parameters. Terrain roughness is defined
as the variability in elevation that can be expressed as the absolute standard deviation of
elevation or standard deviations of slope elevation within a window (Grohmann et al., 2011).
Terrain roughness index reflects the elevation heterogeneity within an area of interest (Riley
and Malecki, 2001). In the literature, many different terms are being used to explain terrain
roughness such as ruggedness (Beasom et al., 1983), rugosity (Wilson et al., 2007), and micro-
topography (Herzfeld et al., 2000). Terrain roughness provides a measure of surface elevation
(or relief) (Grohmann et al., 2011). In this study, terrain roughness correlation with damage
level is examined. We assume that for a damaged building, the terrain roughness of the nDSM
should be higher compared to terrain roughness of nDSM of an intact building. Accordingly, the
possible relationship between surface roughness and damage levels is assessed in this study.
Sensitivity Analysis
To understand the effect of input file on accuracy of results, sensitivity analysis is necessary.
The sensitivity of output is a control parameter for the method that is being used
(Rottensteiner et al., 2007). Different parameters can be used for sensitivity analysis. Object
and pixel based qualities can provide balance between completeness and correctness
(Awrangjeb et al., 2010). Completeness refers to the detection rate of an algorithm to detect
object of interest and correctness is the rate of truly detect objects of interest (Sun et al., 2005).
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STUDY AREA, DATA, AND SOFTWARE
Study Area
The study area is located in the middle of the city of Port-au-Prince around the National
Palace. The area of interest includes more than 64 buildings and it covers up to 200×500 square
meters (Figure 1).
Figure 1 – Study Area: City of Port-au-Prince (http://www.geoeye.com/CorpSite/gallery)
A devastating and widespread earthquake happened in Haiti on January 12, 2010. Haiti was
known as the poorest country in the Western hemisphere and it was ranked 154 of 177
countries in the UN’s Human Development Index (Jobe, 2011). Developing countries cannot
feed themselves after disasters thus aid of volunteers is unavoidable (Millard, 2010).
Furthermore, in developing countries lack of enough resources to help people after a
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widespread earthquake makes rescue and relief efforts more difficult because damages are
more extensive and severe. UNOSAT1 visually surveyed 90000 buildings via satellite images to
determine the level of damages as (a) fully destroyed, (b) severe damage, (c) moderate
damage, and (d) no visible damage. The result of their findings explains that 9-12 percent of
damaged buildings were completely destroyed, 7-11 percent of damaged buildings were
severely damaged, and 5-8 percent of damaged buildings were moderately damaged. In
average between 21-31 percent of buildings were damaged after the Haiti earthquake in 2010.
Study area which has a mixed damage types is selected to understand the effectiveness of our
proposed method.
Data
I collected necessary data from different sources. Three types of data are used in this study:
satellite images, LiDAR data, and VGI. Datasets and their sources are: GeoEye-1 high resolution
satellite image2, LiDAR data provided by RIT-IPLER3, road centerline extracted from OSM data
server4, and building damage maps created by UNOSAT and DLR5.
GeoEye-1 Images
The high resolution satellite image used in this research is GeoEye images of the pre- and
post-earthquake. Its resolution is 0.41 meters for panchromatic images and 1.65 meters for
multispectral images at Nadir (Mattox, 2011). Because Haiti earthquake occurred on 12 January
1 UNITAR’s Operational Satellite Applications Programme (http://www.unitar.org/unosat/maps/HTI) 2 http://www.geoeye.com/CorpSite/gallery/detail.aspx?iid=287&gid=20 3 Rochester Institute of Technology - Information Products Lab for Emergency Response (http://ipler.cis.rit.edu/projects/haiti) 4 http://download.geofabrik.de/central-america/haiti-and-domrep.html 5 Deutsches Zentrum für Luft- und Raumfahrt (http://www.dlr.de/en/DesktopDefault.aspx/tabid-6214/10201_read-22076/gallery-1/gallery_read-Image.1.12786/)
17
2010 two satellite images for a period of 26/08/2009 and 13/01/2010 are downloaded to
reflect pre-earthquake and post-earthquake situations.
Figure 2 – GeoEye-1 Images: Before (left) and after (right) Earthquake (Google Earth)
LiDAR
LiDAR data for the city of Port-au-Prince gathered after the earthquake by IPLER partners
ImageCat Inc. and RIT with respect to the World Bank request. Flight dates were in the period
from 21st – 27th January 2010 (almost 10 days after the earthquake). The horizontal coordinate
system defined as UTM Zone 18N WGS84 Meters and Vertical coordinate system was defined
as Orthometric EGM96. Selected area contains 3773000 points and they are processed and
downloaded from the NSF open topography portal1.
1 http://opentopo.sdsc.edu/
18
Figure 3 - LiDAR Data Over Haitian Palace (http://ipler.cis.rit.edu)
Road Centerline from OSM
In this study we try to incorporate VGI in disaster management processes. After Haiti
earthquake, VGI is created drastically by volunteers. Volunteers contributed in map making and
made information on the OSM website. For a city of Port-au-Prince there were 10000 edits
from people worldwide. Road centerlines are the major point of interest for this study which is
created drastically after the earthquake by volunteers (Chavent, 2011). The major difference
between completeness of road centerlines on OSM is discernible for the city of Port-au-Prince
in Figure 4.
19
Figure 4 – RCL of OSM: Before (left) and After (right) Earthquake (Maron, 2010)
Damage Assessment Map
To assure the quality of damage assessment method, a damage map which is provided by
UNOSAT, DLR, and ITHACA1 is used as a basis to verify the final damage map.
Figure 5 – Damage level map provided by UNOSAT (http://www.unitar.org/unosat)
1 Information Technology for Humanitarian Assistance Cooperation and Action
20
Software and Tools
In this study, three software packages are used for data management, analysis, and
preparation.
1- ArcGIS 10.1®
ArcGIS® mapping and analysis package is the most well-known software for managing and
analyzing geographic data that is developed by ESRI (Environmental Sciences Research
Institute) (ESRI, 2014). LiDAR data specifically analyzed through 3D analyst Arc Toolbox tool to
create nDSM and DEM of the area of interest (ESRI, 2014). Building polygons are managed
through ArcGIS and damage levels have been entered in the attribute table for each building.
OSM data is managed in ArcGIS. OSM data is converted to raster to be able to implement
validation technique and then integrate with nDSM data. Surface roughness indexes are
calculated in ArcGIS for all of the detected buildings.
2- MATLAB 2013®
MATLAB® (Matrix Laboratory) is a high-level language and interactive environment for
numerical computation, visualization, and programming (MATLAB, 2013). In this study, MATLAB
is used for implementing graph-cut and Hough transform algorithms for building extraction.
Basically the programming capabilities of the software package have been used to create
boundaries of buildings according to the given satellite images (MATLAB, 2013). A pre-
developed code by Salah (2011) is modified to implement our method1. It is based on the code
that was developed for fast energy minimization via graph-cut (Boykov and Kolmogorov, 2004;
Boykov et al., 2001; Kolmogorov and Zabin, 2004; Salah et al., 2011).
1 The original code can be downloaded from: http://www.wisdom.weizmann.ac.il/bagon/matlab code/GCmex1.9.tar.gz
21
3- ENVI 4.8®
ENVI® (Environment for Visualizing Images) software is an advanced image processing
toolbox for geospatial analysis which let users to analyze satellite images (ENVI, 2010). In this
study, ENVI is used for: (1) histogram matching of two satellite images at different times (2)
change detection between two satellite images to create damage maps.
22
METHODOLOGY
This study includes two major steps: building extraction and damage assessment. Building
extraction is based on pre-earthquake satellite image as primary data. Damage assessment is
basically done by comparing two images at different times which is also known as change
detection algorithm. LiDAR data and OSM are used to create nDSM and road centerlines,
respectively. The nDSM is also used for calculating terrain roughness indexes for every
extracted building to understand the correlation between damage level and terrain roughness.
A general flowchart illustrating the methodology of this study is depicted in Figure 6 and
major steps are summarized below. The first step includes histogram matching of satellite
images, LiDAR data classification and subset clipping, and assuring the quality of road
centerlines of OSM data. The second step includes two methods of image segmentation for
building extraction: the Hough transform and graph-cut. The third step comprises change
detection algorithm for determining level of damage for each building and nDSM terrain
roughness indexes calculation for each building to correlate damage levels and terrain
roughness indexes. Fourth step includes detection of possible road blockages based on OSM
and nDSM intersection. Finally, sensitivity analysis shows the sensitivity of building extraction
method to the resolution of input images. List of steps are provided in the following part:
1- Data preprocessing
2- Building extraction
3- Building damage assessment
4- Road blockage detection
5- Sensitivity analysis
23
Pre-and-Post earthquake Satellite
Images
Change detection
Pre - earthquake Satellite Image
Building extraction
LiDAR
nDSM generation
OSM
RCL verification
Possible road blockages
Building damage assessment
Terrain roughness
Damaged building roughness indexes
Figure 6 – General Flowchart of the Study
Data Preprocessing
Preprocessing steps on satellite images, road centerlines, and LiDAR data should be utilized
prior to analysis. First, for satellite images the histogram matching is used to create almost the
same lightning conditions for both images. Next, for LiDAR data classification of points to non-
ground and bare-earth is done before any analysis. Third, the reliability of road centerlines of
OSM is assessed to verify the input data validity. In case of low accuracy, the transformation is
used to correct the data.
Histogram Matching
To provide same lighting condition for pre-and-post earthquake images which are taken at
different times, histogram matching is used. In other words, both images of the disaster area
24
should have the same contrast. It is a necessary step before implementing a change detection
method (Helmer and Ruefenacht, 2005). Histogram matching transforms histogram of one of
the image to the other. Histogram matching is usually used for gray scale images because in
color bands histogram matching unequally changes the combination ratios of bands (Yang and
Lo, 2000).
Histograms as well as providing image statistics are being used as the basis for image
enhancement. To put two images at the same lighting condition a transformation function
should be used which is written in the following form (Gonzalez, 2006, pp 142):
𝑆 = 𝑇(𝑟) = (𝐿 − 1)� 𝑝𝑟(𝑤)𝑑𝑤 (1)𝑟
0
- Where r is intensity of a pixel 0≤r≤L-1
LiDAR Data Classification
Before the use of LiDAR information there are several processes that should be done. ESRI
provides a workflow for analyzing and restructuring of LiDAR information which includes four
major steps (ESRI, 2013):
- Calculate basic statistics such as point spacing, density, and resolution
- Create a subset of data based on area of interest (AOI)
- Import LiDAR information into geo-database as multipoint format
- Visual inspection of the data to avoid data voids
Afterwards, some classification and pre-processing steps should be done to better
assimilation with other data types. These steps are categorized into four groups and the
resulting file is Laser data (LAS) (Philips, 2010):
- Isolated point filter
25
- Ground classification (bare earth class)
- Below surface filter (low point class)
- Low points class
The resulting LAS file contains a binary file, X, Y, Z, intensity, return, number, no. of returns,
scan direction, edge of flight line, scan angle rank, user data, point source ID (ESRI). Such a data
can be used for creating a digital surface model (DSM) of the suffered area and normalized DSM
(nDSM) which ultimately gives elevation of buildings (Rottensteiner and Jansa, 2002).
𝑛𝐷𝑆𝑀 = 𝐷𝑆𝑀 − 𝐷𝐸𝑀 (2)
OSM Reliability
The road centerlines reliability is done to verify the accuracy of the voluntarily generated
information. In this study because of insufficient information of OSM road centerlines, I did not
use them in building detection but they are incorporated in road blockage detection. The
proposed method first validates the quality of OSM. Comparison of the overlap percentage
between OSM dataset and professional data set is known as buffer comparison method which
is originally developed by Goodchild and Hunter (1997). In the buffer comparison method, a
buffer within a distance from higher accuracy data set is created and the overlap percentage of
unprofessional created data set with professional data is calculated to verifies the accuracy
unprofessional data within the area of interest (Goodchild and Hunter, 1997); which
unprofessional data is OSM road centerline in this study.
26
Building Extraction and Damage Assessment
Details of building damage assessment and change detection processes have been provided
in the Figure 7. Building damage assessment is based on the pre-earthquake image. Change
detection is based on two images from pre-and-post-earthquake.
Building damage assessment is done with two methods and accuracy of them is compared.
The more accurate method is chosen to generate building polygons. After overlapping with the
result of change detection method, damage level of each extracted building is calculated. Next,
two terrain roughness variables for each building are calculated using nDSM. Finally, the
relationship between roughness variables and building damage levels are discussed.
27
Pre-and-Post earthquake Satellite
Images
Unsupervised Classification
(ISOData method)
Change Detection
Pre - earthquake Satellite Image
Building Extraction by Graph-cut and Hough transform
Building extraction accuracy
assessment
Damage map
Determine more accurate method
Building boundaries
Building damage assessment
LiDAR
nDSM
Generate each building nDSM by
intersection
Terrain roughness variables for each
building
Building roughness category
Building roughness values and damage
relationships
Preprocessing (geo-referencing
and histogram matching)
Change detection Building extraction
Figure 7 – Flowchart of the Building Damage Assessment
28
Building extraction is based on pre-earthquake satellite image of GeoEye-1. The building
extraction method includes localization of each building, building’ edge detection and
verification, creating a virtual line in which boundary lines are inferred from lines of previous
step, and construction building shape (Sohn and Dowman, 2003). Building recognition is
possible after segmenting an image into different objects such as roads, vegetation, buildings,
and etc. For image segmentation multiregional Image segmentation by parametric Kernel
Graph Cuts and Hough transform are used.
Building Extraction by the Hough transform
The Hough Transform is a powerful image processing tool to extract linear features from
images such as roads and building edges (Wang and Liu, 2005). Because in most of the cases
building geometrical shapes are close to a rectangle, the edge detection technique can be used
to modify boundary lines. Candidate points of edges should be connected to represent the
building boundary. In the Hough transform method, for a given point (x, y) the y-intercept can
be calculated from the following equations:
𝑦𝑖 = 𝑎𝑥𝑖 + 𝑏 (3)
𝑏 = 𝑦𝑖 − 𝑎𝑥𝑖 (4)
Then, pixel values of an image are transformed to the parametric domain, called
accumulator space, using the following formula in which r represents length and 𝜃 represents
angle from the origin of a normal to the line (locations of local maxima in parametric domain)
(Gonzalez et al., 2009):
𝑟 = 𝑥𝑐𝑜𝑠𝜃 + 𝑦 𝑠𝑖𝑛𝜃 (5) Finally, for each point a pair of (r, 𝜃) is calculated and then resulting accumulator peaks on
the line correspond the presence of a straight line (San and Turker, 2010).
29
Figure 8 - An Example of Hough Transform and the Corresponding Edge Point: Adopted from
(Lee and Park, 2011)
Building Extraction by the Graph-cut
An image can be represented as a matrix of pixels. It is a directed graph G which it consists
of a set of nodes or vertices V and directed edges E. Vertex set V corresponds to pixels and edge
set represents relationships between pixels. Edges also have weights or costs. For neighboring
pixels or N-links cost corresponds to a penalty for discontinuity between them. Furthermore, T-
link’s weight connects a pixel and a terminal to represent a penalty for assigning a comparable
label to it. If the graph is partitioned into two node sets of S, T with cut C, the cost of a cut C is
the sum of the costs of its boundary edges. From all possible cuts one has the minimum weight
which is one of the fundamental results of optimization problems (Boykov and Kolmogorov,
2004).
30
Figure 9 - An Example of Graph Cuts and the Corresponding Vertex Labeling: Adopted from
(Peng et al., 2013)
The proposed method uses image segmentation techniques to extract objects of interest
from satellite images. The Graph-cut technique has a unique capability in the incorporation of
prior knowledge about objects’ shape. An object of interest is building and the dataset is
composed of GeoEye-1 high resolution satellite images that contain useful information about
the geometry of buildings. All of the graph theory approaches in image segmentation are
categorized into five sub-classes: minimal spanning tree, graph-cut cost function, graph cut
based on Markov random field, shortest path, and other methods (Peng et al., 2013). If G= (V,
E) is a graph with vertex set V and edge set E which is representing an image a graph cut can be
defined as a set of edges, which partitions the graph G into disjoint sets A, B.
Cut (A, B) = � 𝑊(𝑢, 𝑣) (6)𝑢𝜖𝐴,𝑣𝜖𝐵
Another formulation for image segmentation is as a labeling problem, where L labels
assigned to S (pixels or regions).
L = (𝑜𝑏𝑗𝑒𝑐𝑡, 𝑏𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑) (7)
31
For different application distinct piecewise models can be used however for satellite images
it can best described by the Gamma distribution because general Gaussian distribution is not
sufficient for nonlinear and complex domains (Salah et al., 2011). First an image is transformed
via mapping function ф, thus the piecewise model can work on mapped space. More detailed
information is available in the related books and papers. Mapping function can be named as Ф
(.) from an observation space Ι to mapped space ϑ. Every region can be modified as:
𝑅𝑙 = �𝑝 ∈ Ω|𝜆(𝑝) = 𝑙, 1 < 𝑙 < 𝑁𝑟𝑒𝑔� (8)
To solve a graph-cut method each pixel is going to be assigned a label to ultimately
minimize the following equation:
𝐹𝑘({𝑢𝑙}, 𝜆) = � � �𝜙(𝑢𝑙) − 𝜙�𝐼𝑝��2
+ 𝛼 � 𝑟�𝜆(𝑝),𝜆(𝑞)�{𝑝,𝑞}∈𝑁𝑝∈𝑅𝑙𝑙∈𝐿
(9)
- Where L is a set of regions, 𝜆 assigns each image pixel to a region, 𝛼 is a positive factor, 𝑢𝑙 is
a piecewise constant model parameter of 𝑅𝑙 region, 𝑟(𝜆(𝑝),𝜆(𝑞)) is a smoothness
regularization function.
Boundary Smoothing and Generalization
The line boundaries that are derived from building extraction consist of small line segments
and redundant points (Dutter, 2007). As a result, to smooth out jagged lines that are created in
building extraction method, boundary smoothing is implemented (Ahmadi et al., 2010). To
correct noisy polygons, I used the generalization tool in ArcGIS software toolbox (ESRI, 2014).
32
Building Damage Assessment
Change detection by Percent Difference
Image differencing is one of the unsupervised change detection methods which it has been
used most for change detection of satellite images (Bustos et al., 2011). Image differencing is
possible using the single band images and the change detection difference map can
characterize the differences between pair of initial state and final state images. Positive
changes represent the greater brightness values in the final image (ENVI, 2010). For a single
building if the area of changes divided by area of building rooftop, level of damage can be found
(Chun et al., 2008):
𝐷𝑎𝑚𝑎𝑔𝑒 𝑙𝑒𝑣𝑒𝑙 = 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑑𝑎𝑚𝑎𝑔𝑒𝑑 𝑝𝑖𝑥𝑒𝑙𝑠
𝐴𝑟𝑒𝑎 𝑜𝑓 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔× 100 (10)
All buildings in the area of interest will be explored and damage level will be determined.
Different damage levels are classified in table 2.
Table 2 – Building Damage Classification Criteria (Chun et al., 2008)
Degree of damage Criterion
No damage (Intact) No change
Slight damage Damage level<30% rooftop
Moderate damage 30%<Damage level<60%
rooftop
Severe damage 60%<Damage level<90%
rooftop
Complete damage
(Collapsed)
Damage level>90% rooftop
33
Terrain Roughness
To better understand the relationship between building elevations and two indexes for
terrain roughness are calculated in this study. Standard deviation of building elevation and
terrain ruggedness index (TRI) (Riley and Malecki, 2001) are two variables of terrain roughness
that I used in this study. I assumed that damaged buildings might have higher roughness
indexes than undamaged buildings. Standard deviation of elevation is calculated from the
following formula (Ascione et al., 2008):
𝑆𝑇𝐷𝐸𝑙𝑒𝑣2 =𝑚𝑒𝑎𝑛(𝐷𝑆𝑀) − 𝐷𝑆𝑀
𝑟𝑎𝑛𝑔𝑒(𝐷𝑆𝑀) (11)
- Where mean (nDSM) is the average of elevation of over a building rooftop
TRI for a cell is calculated based on the difference between the center cell and eight
adjacent cells. Then, the average of squared differences is calculated to represent the TRI. It is
calculated in ArcGIS using focal statistics in spatial analyst toolbox. Calculation steps are shown
here and then categories of TRI are summarized in the table 3:
𝑇𝑅𝐼 = 𝑆𝑄𝑅𝑇((𝑚𝑎𝑥(𝐷𝑆𝑀) − min(𝐷𝑆𝑀))2) (12)
- Where max and min are maximum and minimum neighborhood statistics of DSM within a 3
by 3 window for a building
34
Table 3 – Terrain Roughness Index Categories (Riley et al., 1999)
TRI category TRI
value
Level TRI<80
Nearly Level 81<TRI<
116
Slightly Rugged 117<TRI
<161
Intermediately
Rugged
162<TRI
<239
Moderately
Rugged
240<TRI
<497
Highly Rugged 498<TRI
<958
Extremely Rugged 959<TRI
Sensitivity Analysis
I used image resampling technique to create different image resolutions for sensitivity
analysis (Szeliski et al., 2010). Many methods are developed for image resampling but I used
nearest neighbor assignment resampling in ArcGIS to create three images with different
resolutions. To evaluate the overall quality of building extraction methods, the extracted
building map is compared with the reference dataset that is generated by UNOSAT. Object
based quality assessment is compared with two parameters: completeness and correctness.
The following formulas can describe how they are going to be computed (Awrangjeb et al.,
2010)
35
𝐶𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑛𝑒𝑠𝑠 = 𝑇𝑃
𝑇𝑃 + 𝐹𝑁 (14)
𝐶𝑜𝑟𝑟𝑒𝑐𝑡𝑛𝑒𝑠𝑠 = 𝑇𝑃
𝑇𝑃 + 𝐹𝑃 (15)
In the equations (9) and (10), TP is the number of true positives or the common entities in
two datasets. FN is the number of false negatives or entities in the reference dataset which
have not been found in the resulting dataset. FP is the number of false positives or number of
detected entities which did not present in the reference dataset. Completeness is also called
detection rate and correctness is called quality percentage.
Possible Road Blockages
In the Figure 10 it has been shown that how possible road blockages are detected by
integrating LiDAR data and OSM. A verified road centerline from OSM has been created to
intersect with nDSM. In the nDSM values represent elevation of non-ground features and
everywhere on the road that there is a positive value, it can represent a road blockage. To
account for inaccuracy of data and other noises, if positive values of nDSM located on the road
and the elevation value exceed 2 meters, it is classified as a possible road blockage (Parks,
2010).
36
LiDAR
nDSM
OSM
Create road centerline (RCL)
Convert to raster
Buffer comparison method for RCL
verification
RCL Buffer (2.5 m) Above ground objects
Intersection
Possible road blockages
Thresholding >2m
Figure 10 – Flowchart of the Possible Road Blockage Detection
37
RESULTS AND DISCUSSION
Histogram Matching
In this study, I have two satellite images that are taken at different times and they have
different lightning conditions. Prior to change detection, I should correct the differences
between lightning conditions. So, histogram matching technique is used. Histogram matching is
implemented on gray scale images. The histogram of the image after the earthquake is
matched to the histogram of the image before the earthquake (Figure 11). Finally, the matched
histogram for the post-earthquake image is shown (Figure 12).
Figure 11– Histograms of Images at Two Different Times
Figure 12 – Matched Histogram of Post-Earthquake Image
38
LiDAR Data Classification and nDSM Generation
To generate DEM, DSM, and nDSM the LiDAR data is analyzed in ArcGIS 10.1. In the
following Figure, the mentioned layers can be seen. The nDSM is used in two steps: possible
road blockage detection and building damage assessment.
Figure 13 – DEM, DSM, and nDSM of City of Port-au-Prince, Haiti, 2010
39
OSM Reliability Analysis
As it is explained earlier, quality of OSM data in terms of positional accuracy is still in doubt
and uncertain. One of the methods for quality control in GIS is implemented by Goodchild and
Hunter (1997) and it is known as buffer comparison method. Based on the spatial resolution of
GeoEye-1 satellite image (0.41 meters at nadir for panchromatic images and 1.65 meters for
multispectral images), an appropriate buffer size is 2 meters.
If the overlapping percentage is not ideal, some edition and correction processes should be
done. In this study, affine transformation is used for spatial adjustment. In table 4 positional
accuracy of road centerline within an area of interest for the OSM dataset is shown. The
average distance between two datasets is 3.75 meters. The base data for comparison is DLR
road network. The minimum overlapping criteria is 80 percent. Based on the initial result of the
buffer comparison method, the overlapping percentage is 57 percent which is not acceptable.
An affine transformation is used to increase the accuracy of the data. The adjusted RCL has an
overlap of 86 percent with DLR (Table 4). The final result is shown in the Figure 15.
Table 4 - Positional Accuracy of Open Street Map Road Centerline
Length (m) Overlap
percentage
DLR road network 8562 100%
Overlapping OSM& DLR 4860 57%
Overlapping Adjusted OSM&
DLR
7336 86%
40
Figure 14 –OSM and DLR Road Networks
Figure 15 – Adjusted OSM and DLR Road Networks
41
Building Extraction
Two algorithms for building extraction have utilized in this study. The first method is the
Hough transform and the second method is the Graph-cut. The accuracy of building extraction
has been evaluated. The result of the better method is then used for the following steps.
Building extraction with Hough transform
For detection buildings with the Hough transform algorithm, I modified codes provided in
Matlab 2013 documentation. First, building edges have been determined in a parameter space
and an accumulator voting procedure has excluded the short length and less vote segments.
Then, the remaining edges with rectangular shape have been superimposed on the original
image to determine the detected buildings. Next, boundary smoothing and generalization is
done to ignore small line segments. The results of this algorithm have shown the total of 39
buildings within the area of interest. The correct number of buildings in that area is 62. In the
Figure 16 the original image and extracted buildings are shown.
42
Figure 16 – Building Extraction Result by the Hough Transform
Building Extraction with Graph-cut
Building extraction with graph-cut algorithm has been utilized in Matlab. The Matlab code
that has been used was previously written by Boykov and Kolmogrov (2004). I modified the
code and then I implemented it for building extraction based on the pre-earthquake image
(Boykov and Kolmogorov, 2004; Kolmogorov and Zabin, 2004; Salah et al., 2011). In the Figure
17 the original image and extracted buildings are shown.
43
Figure 17 – Building Extraction Result by the Graph-Cut
Sensitivity Analysis
In the table 5 the accuracy of graph-cut and Hough transform methods in terms of total
buildings detection (completeness) and object quality (correctness) based on different image
resolution are compared. The initial input image is GeoEye-1 with 0.5 meters resolution and
two other images with 2 meters and 5 meters resolution are resampled from the original image
based on nearest neighbor resampling technique in ArcGIS.
44
Table 5 - Building Extraction Quality Measures with Graph-Cut and Hough Transform
Image
Resolution Method
Detected
Buildings
Complete
ness
Correct
ness
0.5 meters
Hough
transform 39 61%
92%
Graph-cut 52 82% 91%
2 meters
Hough
transform 19 30%
80%
Graph-cut 43 67% 93%
5 meters
Hough
transform 5 8%
71%
Graph-cut 18 28% 95%
The total number of buildings in the area of interest is 64. As we can see the accuracy of
graph-cut for building detection is much higher than the accuracy of Hough transform.
Although for a high image resolution the detection rate is much higher, the time of building
extraction is also much higher for graph-cut. The completeness rate for graph-cut method
based on 2 meters image resolution is higher than Hough-transform while the time of analysis
is much less. According to the results, the 5 meters image resolution cannot result in
satisfactory results for building extraction. Results of graph-cut method using 0.5 meter image
resolution are used for damage assessment.
One possible reason that different image resolutions resulted in different results is that
when we have the image resolution of 5 meters the distinction between two adjacent buildings
is not as clear as the 0.5 meters image resolution. In the lower quality images (higher number of
image resolution), the boundary between buildings become indistinguishable.
45
Different reasons are possible for different results of building extraction using graph-cut and
Hough transform for a specific image resolution. First, Hough transform is more efficient when
the input image is homogenous and includes distinguishable objects. In this study, the area I
focused included the mixture of different types of buildings that the building detection was
difficult even with human interactive helps. Hough transform is not efficient for complex
situations when the distinction between buildings is not very clear (Lowe, 2004). Also, when the
algorithm is not constrained it gives unpredictable results because it cannot detect when a
building is filled and it should stop. It is necessary to provide additional information such as
approximate size and shape of the objects (Vozikis and Jansa, 2008).
On the other hand, the biggest advantage of hough-transform is that it addresses the
segmentation in a global optimization framework to ensure the optimal solution for wide class
of energy functions (Kolmogorov and Zabin, 2004). Another possible reason is that graph-cut
includes both regional and boundary properties together (Peng and Veksler, 2008). Finally, the
determination of objects and backgrounds by user can create more accurate results. One of
the limitations of the graph-cut is the proper selection of parameters at the beginning step
(Boykov et al., 2001).
Building Damage Assessment
To generate damage map of buildings, image differencing change detection algorithm is
utilized on the pre-and-post earthquake satellite images in ENVI (Environment for Visualizing
Images) image processing environment. The following Figure shows the final change map.
Percent difference is calculated after normalizing data ranges of input images. Image
differencing is applied to GeoEye-1 imagery to create damage maps. The final results it the
46
quantified change detection map for the area of interest. Difference image is exported into the
ArcGIS to analyze the building damage levels. In the Chun et al. (2008) five classes of building
damage level are defined. I used the same classification criteria and the final classified building
damage map is presented here.
Figure 18 – Building Damage Assessment
47
Table 6 - Comparison of Building Damage Levels
Damage
Level
Number of
Buildings
Ratio to all
buildings (%)
Intact 1 2
Slight damage 31 60
Moderate
damage 9
17
Severe
damage 6
11
Complete 5 10
Total 52 100
To verify results of our damage assessment, UNOSAT damage map which is generated from
field surveying and satellite image analysis is being used (http://www.unitar.org/unosat/).
Overall, only one building classified as slightly damaged while it was intact and one building
classified as moderate damage while it was severely damaged. Five buildings are classified as
slightly damaged but they were actually moderately and completely damaged.
Terrain Roughness
This step is implemented to understand the possible correlation between terrain roughness
variables and damage levels for each building. I assumed that damaged buildings should have
higher values for terrain roughness variables. Two terrain roughness variables that I examined
for each building are: standard deviation of building elevation and terrain ruggedness index.
First, standard deviation of building elevation for all of the detected buildings and then TRI of
building elevation for same buildings are calculated. In the following Figures results are shown:
48
Figure 19 – Standard Deviation of DSM
Figure 20 – TRI of DSM
49
More focus on the results of terrain roughness variables and building damage levels is
required. In this study, I assumed that damaged buildings should have greater values for
roughness variables. According to the results, there is a positive correlation between roughness
variables and damage level. According to the results I conclude that low roughness variables
represent intact buildings, middle values of roughness variables represent slight and moderate
damages, and high roughness values show severe or complete damages.
Table 7 - Comparison of Building Damage Levels and Terrain Roughness Variables
Damage
level
Averag
e TRI
Average
STD2
Intact 44 <.01
Slight 115 0.016
Moderat
e 239 0.06
Severe 584 0.12
Complet
e 1442 0.15
Possible Road Blockages
To detect possible road blockages, I used nDSM data. Basically, nDSM represent the height
of above ground objects and values of nDSM for road centerlines should be very low. To
exclude cars and other similar objects, I define the bounding limit more than 2 meters. I assume
that any region on the intersection of nDSM and road centerline that have the elevation more
than 2 meters can reflect the road blockage. In spatial analyst in ArcGIS I created another raster
to include only objects with more than 2 meters height. Next, I used road centerlines from OSM
50
portal. They were created by volunteers after Haiti earthquake in 2010 (Chavent, 2011). At the
beginning of this chapter, I explained results of the method I used to verify the quality of the
OSM.
To consider the whole area that a road covers I should create a buffer around the road
centerline. I created a 6 meter buffer around the road centerline because the road width in our
area of interest was 12 meters. Then, I converted the buffered road centerline to raster. In the
ArcGIS the intersection of raster of buffered road and nDSM (values greater than 2 meters),
gave us the possible road blockages. Following Figure represents one possible road blockage
within the area of interest.
Figure 21 – Possible Road Blockage
51
CONCLUSION
Generating damage assessment maps is one of the most important steps that should be
done in the process of response and recovery. In providing such information, time of response
is an important factor because the necessary information is not available. Researchers have
tried to combine different methods and multiple datasets to respond more quickly and
efficiently. Most of prior studies are utilized based on remote sensing information to create
regional damage maps. The limitation of previous methods on assessing damages at building
scale level is one of the motivations for implementing this study. Also, I tried to incorporate
volunteered geographic information in the process of damage assessment.
The purpose of this research was to generate building damage assessment maps using high-
resolution satellite images. Building damage assessment included two main components:
building extraction and damage assessment. In the previous studies, damage maps were
created using satellite images and they were able to reflect the damage at region scale level. In
this study, I combined building extraction and damage assessment methods to detect damages
at building level. Then, I calculated terrain roughness variables using LiDAR data to understand
how post-earthquake LiDAR information can be used for detecting damage level. Results of our
study are in three main groups. First, I showed that the graph-cut is a more accurate method
for building extraction than the Hough transform. Second, I found a strong relationship
between building damage levels and terrain roughness variables. Third, an integration of LiDAR
and OSM is used to create possible road blockage layer.
Two methods for building extraction from satellite images that were implemented in this
study are the graph-cut and the Hough transform. The graph-cut method was never used
52
before in prior studies in this area and this was the first time I used this algorithm for building
extraction. Accuracy of both methods was compared with respect to the building detection
completeness and correctness. Each method was repeated with different satellite image
resolution (0.5 m, 2.5 m, 5 m) that I created by resampling the original GeoEYE-1 image. Results
showed the sensitivity of our method to the input image resolution. Because the accuracy of
graph-cut method was higher than Hough-transform, I used its results for the following steps.
Two sensitivity analysis parameters were calculated. First, completeness of the graph-cut
method was 82 percent compared to completeness of Hough transform which was 62 percent
for 0.5 meter satellite image resolution. Different efficiency of graph-cut and Hough transform
might be due to the complexity of buildings in our area of interest in which the boundaries are
not very distinctive. It makes the building detection more difficult for Hough transform, which
relies on the local maxima of edge boundaries (Lowe, 2004) than the graph-cut, which is based
on the global optimization and boundary conditions together (Peng and Veksler, 2008).
Comparing terrain roughness variables and building damage levels showed there is a strong
correlation between TRI, standard deviation of elevation and building damage levels.
Limitation and Future Studies
Like any other research, this study is also limited in some ways. Results of this study showed
that the graph-cut method for building extraction using high-resolution satellite images takes a
longer time when compared to the Hough transform. Although the completeness parameter
(detection rate) for graph-cut was higher, the complexity of the graph-cut method can delay the
overall process of damage assessment within a large area. For future research, I suggest
improving the graph-cut method that I implemented. In addition, I used OSM road centerlines
53
in combination with LiDAR to detect road blockages. Although I was able to detect the possible
road blockage within our area of interest, I was interested to integrate road blockage detection
and building damage assessment. Future studies should focus more on this problem.
54
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