• Thomas BLASCHKE - Josef STROBL - Julia WEGMAYR (Eds.)

GI_Forum 2021, Volume 9, Issue 1

12th International Symposium on Digital Earth

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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.


Starting 2016, GI_Forum publishes two issues a Year.
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GI_Forum 2021, Volume 9, Issue 1

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8947-3
Online Edition



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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2,
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: bestellung.verlag@oeaw.ac.at
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Testing Transferability of Deep-Learning-Based Dwelling Extraction in Refugee Camps

    Getachew Workineh Gella, Lorenz Wendt, Stefan Lang, Andreas Braun, Dirk Tiede, Barbara Hofer, Yunya Gao, Barbara Riedler, Ahmad Alobaidi, Gina Marciela Schwendemann

GI_Forum 2021, Volume 9, Issue 1, pp. 220-227, 2021/06/29

12th International Symposium on Digital Earth

doi: 10.1553/giscience2021_01_s220

doi: 10.1553/giscience2021_01_s220


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doi:10.1553/giscience2021_01_s220



doi:10.1553/giscience2021_01_s220

Abstract

For effective humanitarian response in refugee camps, reliable information concerning dwelling type, extent, surrounding infrastructure, and respective population size is essential. As refugee camps are inherently dynamic in nature, continuous updating and frequent monitoring is time and resource-demanding, so that automatic information extraction strategies are very useful. In this ongoing research, we used labelled data and high-resolution Worldview imagery and first trained a Convolutional Neural Network-based U-net model architecture. We first trained and tested the model from scratch for Al Hol camp in Syria. We then tested the transferability of the model by testing its performance in an image of a refugee camp situated in Cameroon. We were using patch size 32, at the Syrian test site, a Mean Area Intersection Over Union (MIoU) of 0.78 and F-1 score of 0.96, while in the transfer site, MIoU of 0.69 and an F-1 score of 0.98 were achieved. Furthermore, the effect of patch size and the combination of samples from test and transfer sites are investigated.

Keywords: deep learning, dwelling extraction, refugee camp, transferability, U-net

Published Online: 2021/06/29 11:22:32

Object Identifier: 0xc1aa5572 0x003c9b7c

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Rights: https://creativecommons.org/licenses/by-nd/4.0/