Dwelling Extraction from Satellite Imagery in Refugee Camps: How Different Sample Data-sets Impact the Results of Convolutional Neural Networks (CNN)?

Omid Ghorbanzadeh, Dirk Tiede, Zahra Dabiri, Stefan Lang

Publikation: KonferenzbeitragPosterPeer-reviewed

Abstract

The presented study analyzes the impacts of different samples of training data-set on the results of the CNN-based technique for IDP camps mapping using World view image and provides an accuracy comparison with ground truth database. Different training data-set containing of two main sample images of our target object and the other objects as non-target samples were prepared. The target samples consists of three considered IDP camps namely large buildings, Tent (tunnel shape) and Tent (rectangular shape). Sample images of bare soil with sparse vegetation, other buildings and structures were considered as the non-target. Both target and non-target samples were used in order to train the structured CNN.
OriginalspracheEnglisch
DOIs
PublikationsstatusVeröffentlicht - 6 Juli 2018
VeranstaltungGI_Forum 2018 - Salzburg, Österreich
Dauer: 3 Juli 20186 Juli 2018

Konferenz

KonferenzGI_Forum 2018
Land/GebietÖsterreich
OrtSalzburg
Zeitraum3/07/186/07/18

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