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dc.contributor.authorJyhne, Sander
dc.contributor.authorGoodwin, Morten
dc.contributor.authorAndersen, Per-Arne
dc.contributor.authorOveland, Ivar
dc.contributor.authorNossum, Alexander Salveson
dc.contributor.authorOrmseth, Karianne Øydegard
dc.contributor.authorØrstavik, Mathilde
dc.contributor.authorFlatman, Andrew C.
dc.date.accessioned2023-03-24T12:22:15Z
dc.date.available2023-03-24T12:22:15Z
dc.date.created2023-01-25T20:45:36Z
dc.date.issued2022
dc.identifier.citationNordic Machine Intelligence (NMI). 2022, 2 1-3.
dc.identifier.issn2703-9196
dc.identifier.urihttps://hdl.handle.net/11250/3060342
dc.description.abstractMapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) 1 in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR)2 , the Norwegian Mapping Authority3 , AI:Hub4 , Norkart5 , and the Danish Agency for Data Supply and Infrastructure6 . The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with or without aerial images. Furthermore, we use IoU and Boundary IoU [1] to properly evaluate the precision of the models, with the latter being an IoU measure that evaluates the results’ boundaries. We provide the participants with a training dataset and keep a test dataset for evaluation.
dc.language.isoeng
dc.titleMapAI: Precision in BuildingSegmentation
dc.title.alternativeMapAI: Precision in BuildingSegmentation
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.pagenumber1-3
dc.source.volume2
dc.source.journalNordic Machine Intelligence (NMI)
dc.identifier.doi10.5617/nmi.9849
dc.identifier.cristin2115134
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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