Urban Sprawl Analysis in Kutupalong Refugee Camp, Bangladesh

Filip Loncar

and Pedro Cabral

NOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal

Keywords: Urban Sprawl, Refugee Camp, Unmanned Aerial Vehicle, Support Vector Machine, Maximum Likelihood

Classification.

Abstract: Urban sprawl is a common phenomenon associated with geographical and political challenges such as refugee

settlements and environmental extremes. Urban sprawl related to refugee or habitation settlement has been

an area of active interest because of humanitarian and environmental problems. For example, higher rates of

urban sprawling are positively correlated with higher rates of deforestation. The present study explored the

viability and reproducibility of different classification techniques in assessing urban sprawl among Rohingya

refugees in the Kutupalong refugee camp in Bangladesh. These classification methods include the Support

Vector Machine (SVM) and Maximum Likelihood Classifier (MLC). The urban sprawl was measured based

on the classification of urban and non-urban classes. The SVM yielded better overall accuracy performance

compared to MLC classification. The study showed that urban class exhibited exponential growth from 2.01

to 5.37 km

within nine months. On the contrary, the non-urban class shrunk from 12.58 km

to 9.95 km

during the same period due to a high influx of refugees and rapid camp expansion.

1 INTRODUCTION

Remote sensing has become popular in the field of

humanitarian action because it is an independent and

reliable source of information that allows both a quick

response to emergencies and monitoring of gradual

changes that are associated with human settlements,

including rehabilitation, sprawling, migration, and

refuge (Lang et al., 2020, Braun et al., 2016 ;

Blaschke et al., 2014 Lang et al., 2015). Remote

sensing is extremely important when observations in

the field are not possible manually due to limited

budget, legal barriers, and security aspects (Chen,

n.d.). The observation of specific places from space is

not only crucial for decision making involving

responses to natural disasters and emergencies

concerning the human race but also helps to develop

a general understanding of an area and the way trends

and temporal dynamics have shaped the special and

spatial patterns (Chang et al., 2011; Bello & Aina,

2014).

Approximately one million refugees of the

Rohingya minority population in Myanmar crossed

the border to Bangladesh seeking shelter from a

systemic operation and prosecution (Faye, 2021).

https://orcid.org/0000-0002-1518-8531

https://orcid.org/0000-0001-8622-6008

This caused significant expansions of the Kutupalong

refugee camp within two months and a reduction in

the vegetation in surrounding forests. Different

humanitarian and Human Rights Organizations

demanded frameworks camp monitoring and

environmental impact analysis (Sahana et al., 2019).

The refugee camp is situated in Ukhia, CoxBazar,

Bangladesh. The oppressions and extortions of the

Rohingya refugees caused the Kutupalong refugee

camp to expand (Braun et al., 2019a)The refugee

camps nowadays are more permanent than simple

transitory settlements, therefore are considered as

urban areas. The variables such as size, population

density, layout, infrastructure concentration, socio-

occupational profile, and trading activities are

supporting factors to consider refugee camps as urban

areas (Montclos & Kagwanja, 2000). The present

work provides novel information by exploring the

urban sprawl dynamics of the Rohingya refugees at

the Kutupalong refugee camp through remote sensing

techniques.

The research question focuses on how

urbanization has changed following the outbreak of

the Rohingya emergency by analysing four (4) drone

Loncar, F. and Cabral, P.

Urban Sprawl Analysis in Kutupalong Refugee Camp, Bangladesh.

DOI: 10.5220/0010970200003185

In Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2022), pages 83-90

ISBN: 978-989-758-571-5; ISSN: 2184-500X

images from 2017 and 2018, by answering the

following questions:

1. Which machine learning classifier

technique yields better performance in urban sprawl

classification in the Refugee camp context?

2. How much km

has urban class increased

over the period of 9 months in Kutupalong Refugee

Camp?

2 DATA AND METHODS

2.1 Study Area

Kutupalong refugee camp is located in south-eastern

Bangladesh along the border with Myanmar. The

camp administrative area is defined by the following

coordinates 21.2126°N 92.1634°E, and with a total

area of 14.5 km

, it hosts a population of 860,356

registered refugee individuals and 187,423 families

(as of June 30, 2020, UNHCR). When Rohingya

minority left Myanmar's adjacent Rakhine state as a

consequence of religious and ethnic persecution,

which culminated in brutal crackdowns and

systematic executions beginning on August 25, 2017,

Kutupalong became the world's biggest refugee

camp. The study area in this research focuses on

Kutupalong camp and its extensions located in Ukhia

Upzalla in the district of Cox’s Bazar region and its

extensions. Figure 2 below represents the study area.

Kutupalong area is situated on a mix of plains and

small hills, extending into the Chittagong Hill tracts

bordering Myanmar. The area is characterized by

heavy rain on the Chittagong Hill tracts has resulted

in numerous landslides. The administration of the

district has introduced a policy on restricted tree-

cutting to limit erosion in the hope of limiting further

landslides and related fatalities (Braun et al., 2019b).

Figure 1: Study Area.

2.2 Data

The methodology for the present study included the

acquisition of the images from IOM Bangladesh –

Needs and Population Monitoring (NPM) Cox's

Bazar Rohingya Refugees Settlements UAV

Imagery, which were stored at data.humdata.org. The

retrieved image packaged were downloaded in tiff

format with mosaiced images. The images were

combined and georeferenced, with Red, Blue, and

Green bands. The dates of the images were taken

from December 2017 to February, July, and

September 2018 following the outbreak of violence in

August 2017 in Rakhine State, Myanmar. The

Imagery type is UAV with a resolution of 10 cm,

projection WGS84 _Zone 46 N. While there were

images from the entire Cox’s Bazar Refugee and

different camps stored in the repository, this research

only focuses on the Kutupalong camp, located in

Cox’s Bazar region. The information on flight

altitude and swath with is unknown to researchers.

The spatial database in shapefile format with an

outline of camps, settlements, and sites where

Rohingya refugees are staying in Cox’s Basar has

been acquired from data.humdata.org provided by the

Inter Section coordination Group. The database

contains the camp-block boundary (admin level-2 or

camp sub-division) of Rohingya refugees in Cox's

Bazar, Bangladesh. Since the research is excluding

the remaining camps of in Cox’s Bazar disrtirct, the

shapefile was clipped leaving out only the

Kutupalong camp and its extensions.

The shapefile of the study area is divided per

camp into 23 sub-regions (polygons) with a total area

consisting of 14.5 km

. The biggest camp is Camp 4

with an area of 1.15 km

being on the northwest side

of the study region to the smallest being camp 6 on

the mid-eastern side with 0.36 km

2.3 Methods

The diagram in Figure 2 explains the workflow of the

research. The study is based on four different images

from 4 different dates to analyse urbanization within

the area of sprawl. Moreover, this research explores

different remote sensing supervised classification

methods (Support Vector Machine and Maximum

Likelihood Classification) to assess which performs

better analysing urbanization in refugee settlements.

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

Figure 2: Data and methodology used for image

classification and result validation.

2.3.1 Supervised Classification

The suggested technique consists of two-different

classification assessments. The performance of

various classifiers with found distinct dataset

combinations from different dates in the same study

area, to determine which of the two will yield superior

results for urbanization. As one of the aims of the

research is to understand which supervised

classification provides the best results for mapping

the urbanization in refugee settlements, MLC and

SVM were tuned in.

To detect and understand urbanization, the

authors have decided to create two classes – Urban

and Non-Urban. Table 1 explains the categories and

definitions of the classes:

Table 1: Classification nomenclature.

Class Level 1 class Description

Urban Residential Refugee housing

units

Commercial Residential areas

Industrial Warehouses

Non-Urban Agriculture Farmlands

Green Space Grasslands,

shrublands

Waterbody Natural or

artificial

waterbodies

Undeveloped Vacant land, bare

land or land

under

construction

2.3.2 Image Classification Accuracy

When evaluating the effectiveness of a classification

model applied to remote sensing data, accuracy

measures are used to determine how near the model's

predictions are to reality. As a result, accuracy

evaluation compares the predicted labels assigned to

an item using an MLC to its actual label using

ground-truth data (test dataset).

A confusion matrix (or error matrix) is commonly

used to determine classification accuracy. The

classification accuracy is a confusion matrix table

shows a correspondence between the classification

result and reference image (images being ground truth

data in this research). This enables for more in-depth

examination than a simple fraction of the right

classifications (accuracy). If the data set is

imbalanced, that is, when the number of observations

in various classes varies considerably, accuracy will

produce false results (Maxwell & Warner, 2020)

From the confusion matrix, and following

research development, we can compute several

accuracy metrics, such as:

 Overall Accuracy: calculated by summing the

number of correctly classified values and

dividing by the total number of values.

 User’s accuracy: The probability is calculated

by dividing the number of properly predicted

values by the total number of values projected to

belong to a class. The user’s accuracy is from

the standpoint of the map user.

 Producer accuracy: The number of properly

identified pixels in each category (on the major

diagonal) divided by the number of reference

pixels “known” to be of that category yields the

following results (the column total).

 Kappa Coefficient: The kappa coefficient

assesses the degree of agreement between

categorization and truth values. A kappa value

of one indicates complete agreement, whereas a

value of zero indicates no agreement.

2.3.3 Urban Sprawl Analysis with

Shannon’s Entropy

The study aims to analyze the process of the built-up

campsites over a period of 9 months in Cox’s Bazaar,

Bangladesh. While there is a wide variety of metrics

that are used to measure the degree of urban sprawl,

this research has adopted the Shannon’s Entropy

Index. The Shannon Entropy is an index or

indication, which is capable of computing spatial

concentration or dispersion in any spatial unit. The

entropy values vary from 0 to 1, which 0 means that

entropy values are maximally concentrated in one

region, while 1 means that values are unevenly

dispersed across space. The entropy value increases

as built-up regions are dispersed from a city core or

road network. This demonstrates whether the urban

growth is more scattered or dense (Tewolde & Cabral,

2011). The model is calculated using the formula

below:

𝐻𝑛   𝑝𝑖





log



1/𝑝𝑖



/log



𝑛



Urban Sprawl Analysis in Kutupalong Refugee Camp, Bangladesh

Where 𝑝𝑖  𝑥𝑖 /

∑

𝑥𝑖





and 𝑥𝑖 is the density of

land development, which is equal to the amount of

built-up land divided by the total amount of land in

the 𝑖th of 𝑛 total zones.

3 RESULTS

3.1 Support Vector Machine

We have deployed ArcGIS Pro for supervised image

classification. Support Vector Machine (SVM)

supervised classification has been performed over the

4 UAV Images from the Kutupalong Refugee Camp

region from 4 different dates to detect urban sprawl

and camp expansion. Since Analysing UAV images

with high resolution requires stronger computing

power, and after multiple failed attempts to perform

SVM classification using original resolution, the

image size was reduced using resample function in

ArcGIS Pro to X being 0.5 and Y being 0.5. When

resampling the image, we understand that the

resolution of the file with be altered, however since

computational power was limited we have decided to

accept the risk.

Image segmentation is a key component in object-

based classification workflow. The segmented

images produced have grouped neighboring pixels

together, that are similar in color and shape. The

segmented images produced for 4 different dates were

acceptable. Following acceptance of segmented

images, the training samples for 2 classes were

collected for urban and non-urban classes. SVM has

been performed with default 500 maximum number

of samples per class, with active chromaticity color

and means digital number activated.

Maps in Figure 3 are outputs of SVM supervised

classification. The red polygons are representing

urban areas, while the grey ones are non-urban.

Table 2 SVM: Representing the numbers of square

kilometers (km

) for each of the classes on each date.

Class/Date 24-12-17 12-02-18 31-07-18 24-09-18

Urban 2.01 2.91 4.63 5.37

Non-Urban 12.58 11.68 9.22 9.95

Upon completion of classification, the researcher

will calculate the actual classified areas in square

kilometers from all the 4 map outputs to compare the

results. Firstly, the SVM classified maps will be

converted to vector layer that is shapefile, using

reclassified and export raster to polygon functions.

Upon conversion, using polygons that have the same

class were merged Calculate Geometry Attributes

function was performed in order to n calculate the

total square kilometer area of each class and compare

between the dates.

Figure 3: Support Vector Machine - Kutupalong Refugee

Camp.

Area wise statistics has been calculated in ArcGIS

Pro with above table 2 representing a number of km

for four different dates for each of our classes, we can

conclude that the urban class has an exponential

growth for only around 9 months from 2.01 km

the first image of 24/12/2017 to 5.37 km

on the last

image of 24/09/2018, which reads 13.7% of the total

area on the first image to 35% total on the last image

taken. The non-urban class however has reduced

from 12.58 km

to 9.95 km

(from 86 % of the total

area to 65%). The SVM classifier is showing higher

are for non-urban class on 31-07-18 date compared to

24-09-18 date (9.22 km

to 9.95 km

)

Even though the time span of the 3rd and 4th

image is not the longest (2 months compared to 2nd

and 3rd image which is around 5 months) we can

notice the highest growth of urban class between 31-

07-18 and 24-09-18, from 4.63 km

to 5.37 km

3.2 Maximum Likelihood Classification

MLC has been performed over the four images from

different dates in the study area. Following the same

methodology as for the SVM classifier, the researcher

is interested to see the urbanization over the study

area in Kutupalong Camp, Bangladesh, two different

classes were used – Urban and Non-Urban. Training

samples in form of polygons were collected for each

class as follows. The MLC showed good performance

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

in classifying very high spatial resolution images as it

was not failing during the classification run process,

unlike the SVM classification. However, to fairly

compare the two classifications, we performed the

MLC classification with resampled in order to fairly

compare the classification performances.

The maps in Figure 5 below are the results of

supervised classification using a Maximum

Likelihood Classification. The blue polygons indicate

the classified urban class over the study area.

After classification is complete, the actual

classified area in square kilometers for all four maps

has been calculated, to understand the classification

by calculating the geometry attributes of urban and

non-urban classes.

Table 3: Representing a number of square kilometers (km

)

for each of the classes on each date.

Class/Date 24-12-17 12-02-18 31-07-18 24-09-18

Urban 3.2 3.6 5.25 7.8

Non-Urban 11.3 10.9 9.25 6.7

Unlike in the case of SVM, with MLC we can

understand that the most significant change in terms

of urban class expansion is between the third and last

image dates (31-07-18 to 24-09-18), which

percentage-wise gives an increase from 36% of the

total area on the third image, to 54% of the total area.

In the case of the non-urban class, the same timespan

(31-07-18 to 24-09-18) gives the highest decrease in

terms of the area from 9.25 km

to 6.7 km

(percentage-wise this gives us a decrease from 64%

to 46%).

Figure 4: Maximum Likelihood Classification - Kutupalong

Refugee Camp.

3.3 Accuracy Assessment

The main goal of the research is to evaluate the

performance of the two classifiers over the different

time periods and to determine which of the classifiers

produces superior results.

For each of the classified images (SVM and MLC)

the random 100 points have been computed in

ArcGIS Pro in order to get the accuracy metrics. The

related UAV image for each date has been used for

ground truth testing against the 100 random points

produced. After the ground truth comparison with

classifiers, confusion matrices have been computed to

get overall accuracy, user accuracy, producer

accuracy, and Kappa Index of agreement.

The results indicate that both classifiers scored

high overall accuracy and performed well when

classifying UAV imagery in an environment such as

refugee camp settlements.

Table 4: Confusion Matrix - Support Vector Machine.

Class/

Date

24-12-17 13-02-18 31-07-18 24-09-18

OA=85% OA=90% OA=94% OA=83%

UA PA UA PA UA PA UA PA

Urban 100% 48% 90% 69% 96% 91% 91% 67%

Non-

Urban

83% 100% 90% 97% 94% 97% 79% 95%

Kappa 0.57 0.72 0.89 0.64

The overall accuracy yielded good results for the

SVM classifier, having a minimum value of 83% and

a maximum value of 85%. User accuracy and

producer accuracy also showed favorable results,

with exception of 48% of producer accuracy of urban

class for the first date of SVM classified image. The

kappa coefficient and the degree of agreement

between categorization and truth values vary from

0.57 for the first image to 0.89 for the third image.

Table 5: Confusion Matrix - Maximum Likelihood

Classification.

Class/

Date

24-12-

13-02-18 31-07-18 24-09-18

OA=81

OA=86

OA=87

OA=85

Urban

Non-

Urban

Kappa 0.54 0.65 0.71 0.69

Urban Sprawl Analysis in Kutupalong Refugee Camp, Bangladesh

Maximum Likelihood Classification has

produced similar results in comparison to SVM

classified. The minimum overall accuracy is 81% for

the first image and varies to a maximum of 87% for

the third image. User accuracy and producer

accuracy have shown good scores however slightly

less when in comparison to the SVM classifier. The

Kappa coefficient and degree of agreement between

categorization and true values is varying from 0.54

for the first image to 0.71 for the third image.

3.4 Change Detection: Support

Vector Machine

To visualize the urban sprawl and detect changes

between the dates, Change Detection Wizard of

ArcGIS Pro has been utilized. The categorical change

method of change detection has been configured over

the 4 classified raster images. Processing was set to

the full extent and class configuration is as follows:

• From Classes: Non-Urban

• To Classes: Urban and Non-Urban

This would give us the outputs where Non-Urban

pixels have changed to Urban class, and where Non-

Urban Class did not change. The smoothing

Neighbourhood was set to none. Figure 4 represents

the change analysis of subsequential images from the

first to the last date.

Figure 5: Change Detection Maps - Support Vector

Machine.

With overlaying the study area extent which has

information on sub-camps and the classified raster

image, we can compute the camp-wise statistics of

SVM supervised classification for four (4) different

dates, to understand the direction urban sprawl is

taking.

In terms of urban class, Camp 3, Camp 1W,

Kutupalong RC, and Camp 6 had the highest value of

urban class on the first image (24/12/2017) with 53%,

32%, 25%, and 23% respectively, while in the case of

the last image (24/09/2018) Camp 10, Camp 11,

Camp 12 and Camp 13 showed highest values of

urban class with 70%, 59%, 58%, and 57%

respectively. However, the highest increase of urban

class within camps compared between the first and

the last image can be noticed in Camp 10, Camp 11,

Camp 12, and Camp 13 with an increase of 45%, 43%

33%, and 32% respectively. These camps that show

the highest change in terms of urbanization are all

located in the south of the study area.

3.5 Shannon’s Diversity Index

Shannon’s Diversity Index shows us species diversity

in the community. In the context of this research, it is

giving us an understanding of the urban sprawl

composition and class richness and evenness over

time (Bourne & Conway, 2014). The diversity index

of the urban class for SVM classifier for dates 24-12-

2017, 13-02-2018, 31-07-2018 and 24-09-2018

0.4 , 0.5 , 0.65 and 0.62 respectively. The trend of

results of Shannon’s diversity index for both

classifiers shows lower diversity between the first two

image dates, however, it has an exponential growth in

value for the third and fourth dates. This has shown

that the urban sprawl in the Kutupalong refugee camp

has increased, due to the influx of refugees and urban

expansion as a need for more housing. If the urban

class is unevenly distributed throughout space, the

increased value of Shannon’s Diversity index reflects

that (K. Madhavi Lata et al., 2009).

Table 6: Evolution of Shannon's Diversity Index for MLC

and SVM Classifier.

Date/

Classifier

24/12/1

12/02/1

31/07/1

24/09/1

SVM 0.40 0.50 0.62 0.65

4 DISCUSSION

In terms of computing power, MLC has an advantage

in analysing very high spatial resolution imagery.

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

MLC classification has successfully analysed original

10 cm resolution images, while the SVM has failed to

do so. Finally, to create a fair comparison, we decided

to reduce and change the spatial resolution of raster

datasets and set rules for aggregating or interpolating

values across the new pixel size to 0.5.

The Kappa coefficient corrects standardized

measures of agreement between two categorical

scores produced by the two rates. Based on Landis

and Koch measurement of observer agreement The

Kappa interpretation of SVM classification gives us

an understanding that agreement is substantial for

values of 0.57, 0.72, and 0.64 and almost perfect

agreement for 0.89. The values for MLC

classification have a similar trend of values where

classification of images 1-4 have values of 0.54, 0.65,

0.71, and 0.69 respectively. In a comparison of the

two classifications, the Kappa coefficient for the

MLC classifier shows higher agreement with

exception of the last-date image where MLC yields

better results.

These results answer the research question,

indicating that the SVM classifier is superior and

gives better performance in classifying urban classes,,

that is refugee settlements in the context of the

research.

When it comes to calculating urbanization, the

research indicates that there has been an exponential

expansion of urban class from 24-12-17 to 24-09-18

from 2.01 km

to 5.37 km

for SVM. The non-urban

class however reduced from 12.58 km

to 9.95 km

The results found in the research are relevant for

urban sprawl analysis in refugee camp settlement and

Humanitarian actors.

The evolution and increase in the values of

Shannon’s Diversity Index indicate that there is an

increase in urban sprawl and development tends to be

more dispersed over a period of time. This indicates a

rapid increase in urban sprawl. The results of this

index give us the idea of spatiotemporal patterns of

urban growth in Kutupalong Refugee camp.

5 CONCLUSION

We demonstrated the application of remote sensing

classification techniques using 4 UAV images from

different dates to identify and calculate the urban

sprawl in Kutupalong Refugee Camp, Bangladesh

which is under great urban expansion due to the influx

of Rohingya refugees from neighbouring Myanmar.

The Rohingya emergency was one of the biggest

crises in 2017, which has severely affected the

change of the physical landscape of the host

community in Bangladesh.

The research analysed the expansion of the

refugee camp from 2017 to 2018. The objective was

to understand which of the techniques yielded better

results. The research was conducted to understand

and evaluate the performance and agreement of two

different machine learning classifiers – Support

Vector Machine and Maximum Likelihood

Classification.

To answer the research question of which

machine learning classifier technique yields better

performance in urban sprawl classification in

Refugee camp context, both of the classifiers’

performances were similar in terms of overall

accuracy for both of the classes under analysis. In

terms of overall accuracy, the advantage has been

given to SVM classifier as it produced slightly better

results.

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