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dc.contributor.authorØrstavik, Mathilde
dc.contributor.authorMidtbø, Terje
dc.date.accessioned2019-04-02T11:29:12Z
dc.date.available2019-04-02T11:29:12Z
dc.date.created2019-01-11T17:03:34Z
dc.date.issued2018
dc.identifier.citationKart og Plan. 2018, 111 (3), 221-230.nb_NO
dc.identifier.issn0047-3278
dc.identifier.urihttp://hdl.handle.net/11250/2592915
dc.description.abstractThis paper explores how machine learning can be used to recognize buildings in aerial images (orthophotos). The Norwegian map data: Joint Map Database (Felles kartbase) is used as «the true value» for training neural networks to detect buildings in aerial images. The paper introduces two models for machine learning. One basic model ( AirNet) and one model which include more layers in the network (AirNet-extended). The second model is supposed to detect more complex shapes in the images. Several parameters for training the neural network are tested. Complete building maps can still not be made using machine learning, but our results show that these methods can be used, for example, to study of city growth, find unregistered buildings etc.nb_NO
dc.language.isoengnb_NO
dc.publisherFagbokforlaget og Norges Jordskiftekandidatforeningnb_NO
dc.relation.urihttp://kartogplan.no/Artikler/KP3-2018/Bruk%20av%20maskinlaering%20for%20aa%20gjenkjenne%20bygninger%20i.pdf
dc.titleBruk av maskinlæring for å gjenkjenne bygninger i flybildernb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber221-230nb_NO
dc.source.volume111nb_NO
dc.source.journalKart og Plannb_NO
dc.source.issue3nb_NO
dc.identifier.cristin1655286
dc.description.localcodeThis article will not be available due to copyright restrictions (c) 2018 by Fagbokforlaget:nb_NO
cristin.unitcode194,64,91,0
cristin.unitnameInstitutt for bygg- og miljøteknikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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