Paper Info
Title
Understanding a Rapidly Expanding Refugee Camp Using Convolutional Neural Networks and Satellite Imagery
Abstract
In summer 2017, close to one million Rohingya, an ethnic minority group in Myanmar, have fled to Bangladesh due to the persecution of Muslims. This large influx of refugees has resided around existing refugee camps. Because of this dramatic expansion, the newly established Kutupalong-Balukhali expansion site lacked basic infrastructure and public service. While Non-Governmental Organizations (NGOs) such as Refugee Relief and Repatriation Commissioner (RRCC) conducted a series of counting exercises to understand the demographics of refugees, our understanding of camp formation is still limited. Since the household type survey is time-consuming and does not entail geo-information, we propose to use a combination of high-resolution satellite imagery and machine learning (ML) techniques to assess the spatiotemporal dynamics of the refugee camp. Four Very-High Resolution (VHR) images (i.e., World View-2) are analyze to compare the camp pre-and post-influx. Using deep learning and unsupervised learning, we organized the satellite image tiles of a given region into geographically relevant categories. Specifically, we used a pre-trained convolutional neural network (CNN) to extract features from the image tiles, followed by cluster analysis to segment the extracted features into similar groups. Our results show that the size of the built-up area increased significantly from 0.4 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in January 2016 and 1.5 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in May 2017 to 8.9 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in December 2017 and 9.5 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in February 2018. Through the benefits of unsupervised machine learning, we further detected the densification of the refugee camp over time and were able to display its heterogeneous structure. The developed method is scalable and applicable to rapidly expanding settlements across various regions. And thus a useful tool to enhance our understanding of the structure of refugee camps, which enables us to allocate resources for humanitarian needs to the most vulnerable populations.
Year
DOI
Venue
2019
10.1109/eScience.2019.00034
2019 15th International Conference on eScience (eScience)
Keywords
DocType
ISBN
Refugee Camp,Rohingya,Convolutional Neural Networks,Satellite Imagery,Machine Learning,Feature Extraction
Conference
978-1-7281-2452-0
Citations 
PageRank 
References 
0
0.34
3
Authors
7
Name
Order
Citations
PageRank
Susanne Benz100.34
Hogeun Park200.34
Jiaxin Li300.34
Daniel Crawl424321.02
Jessica Block5123.63
Mai H. Nguyen600.34
Ilkay Altintas71191106.09