Refugee camp population estimates using automated feature extraction

Brandon Green, Justine I. Blanford

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
18 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationProceedings of the 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020
EditorsTung X. Bui
PublisherIEEE
Pages2197-2206
Number of pages10
ISBN (Electronic)9780998133133
Publication statusPublished - 2020
Externally publishedYes
Event53rd Hawaii International Conference on System Sciences, HICSS 2020 - Grand Wailea, Maui, United States
Duration: 7 Jan 202010 Jan 2020
Conference number: 53

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2020-January
ISSN (Print)1530-1605

Conference

Conference53rd Hawaii International Conference on System Sciences, HICSS 2020
Abbreviated titleHICSS 2020
Country/TerritoryUnited States
CityMaui
Period7/01/2010/01/20

Keywords

  • GIS
  • Object-based
  • Pixel-based
  • Population estimate
  • Python
  • Geo-health
  • geo-health
  • Earth Observation data

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