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

ii

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.

iii

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

iv

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

v

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

vi

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

vii

21 – Possible Road Blockage........... ........................................................................................ 51

viii

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.

1

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.

2

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

3

- 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.

4

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

5

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.

6

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

7

- 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).

8

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

9

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) .

10

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)

11

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

12

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).

13

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).

14

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).

15

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

16

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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