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Refugee Camp Population Estimates Using Automated Feature Extraction

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dc.contributor.author Green, Brandon
dc.contributor.author Blanford, Justine
dc.date.accessioned 2020-01-04T07:36:12Z
dc.date.available 2020-01-04T07:36:12Z
dc.date.issued 2020-01-07
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/64009
dc.description.abstract Throughout 2018, approximately 68.5 million people were forcibly displaced due to armed conflict, generalized violence, or human rights violations around the world; of those, 40 million were internally displaced persons (IDP), 25.4 million refugees, and 3.1 million asylum-seekers. Effective management of refugee and IDP camps rely on accurate, up-to-date, and comprehensive population estimates. However, obtaining this information is not always easy. Thus, the purpose of this study was to develop a methodology and custom toolset that estimates populations based on dwellings derived from automated feature extraction of high-resolution, multi-spectral orthorectified imagery. Estimates were determined for five Rohingya refugee camp populations and compared with United Nations High Commissioner for Human Rights (UNHCR) baseline data to determine accuracy.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Disaster Information, Technology, and Resilience
dc.subject gis
dc.subject object-based
dc.subject pixel-based
dc.subject population estimate
dc.subject python
dc.title Refugee Camp Population Estimates Using Automated Feature Extraction
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2020.268
Appears in Collections: Disaster Information, Technology, and Resilience


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