Visual Analytics for Multitemporal Aerial Image Georeferencing

dc.contributor.authorAmor-Amorós, Alberten_US
dc.contributor.authorFederico, Paoloen_US
dc.contributor.authorMiksch, Silviaen_US
dc.contributor.authorZambanini, Sebastianen_US
dc.contributor.authorBrenner, Simonen_US
dc.contributor.authorSablatnig, Roberten_US
dc.contributor.editorMichael Sedlmair and Christian Tominskien_US
dc.date.accessioned2017-06-12T05:16:30Z
dc.date.available2017-06-12T05:16:30Z
dc.date.issued2017
dc.description.abstractGeoreferencing of multitemporal aerial imagery is a time-consuming and challenging task that typically requires a high degree of human intervention, and which appears in application domains of critical importance, like unexploded ordnance detection. In order to make a semi-automatic scenario possible, we introduce a Visual Analytics approach for multitemporal aerial image georeferencing designed in close collaboration with real-world analysts that face the problem on a daily basis, and implemented by combining computer vision and interactive visual exploration methods. We report on informal validation findings resulting from the integration of our solution into our users' GIS platform of choice, which positively illustrate its effectiveness and time-saving potential.en_US
dc.description.sectionheadersSpace and Time
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20171120
dc.identifier.isbn978-3-03868-042-0
dc.identifier.pages55-59
dc.identifier.urihttps://doi.org/10.2312/eurova.20171120
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20171120
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization
dc.subjectVisualization application domains
dc.subjectVisual Analytics
dc.subjectComputing methodologies
dc.subjectComputer vision problems
dc.subjectMatching
dc.titleVisual Analytics for Multitemporal Aerial Image Georeferencingen_US
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