DC FieldValueLanguage
dc.contributor.authorLuft, Jonas Frederik-
dc.contributor.authorSchiewe, Jochen-
dc.date.accessioned2022-08-02T13:07:25Z-
dc.date.available2022-08-02T13:07:25Z-
dc.date.issued2021-12-03-
dc.identifier.citationProceedings of the International Cartographic Associationen_US
dc.identifier.urihttps://repos.hcu-hamburg.de/handle/hcu/687-
dc.description.abstractIn recent years, libraries have made great progress in digitising troves of historical maps with high-resolution scanners. Providing user-friendly information access for cultural heritage through spatial search and webGIS requires georeferencing of the hundreds of thousands of digitised maps. Georeferencing is usually done manually by finding “ground control points”, locations in the digital map image, whose identity is unambiguous and can easily be found in modern-day reference geodata/mapping data. To decide whether two symbols from different maps describe the same object, their semantic and spatial relations need to be matched. Automating this process is the only feasible way to georeference the immense quantities of maps in conceivable time. However, automated solutions for spatial matching quickly fail when faced with incomplete data – which is the greatest challenge when comparing maps of different ages or scales. These problems can be overcome by computing map similarity in the image domain. Treating maps as a special case of image processing allows efficient and robust matching and thus identification of geographical regions without the need to explicitly model semantics. We propose a method to encode worldwide reference VGI mapping data as image features, allowing the construction of an efficient lookup index. With this index, content-based image retrieval can be used for both geolocating a given map for georeferencing with high accuracy. We demonstrate our approach on hundreds of map sheets of different historical topographical survey map series, successfully georeferencing most of them within mere seconds.en
dc.language.isoenen_US
dc.publisherCopernicusen_US
dc.subjectimage analysisen
dc.subjectautomatic georeferencingen
dc.subjecthistorical mapsen
dc.subjectVGIen
dc.subject.ddc004: Informatiken_US
dc.titleContent-based Image Retrieval for Map Georeferencingen
dc.typeinBooken_US
dc.relation.conference30th International Cartographic Conference (ICC 2021), 14–18 December 2021, Florence, Italyen_US
dc.type.dinibookPart-
dc.type.driverbookPart-
dc.rights.cchttps://creativecommons.org/licenses/by/4.0/en_US
dc.type.casraiBook Chapter-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:1373-repos-8829-
tuhh.oai.showtrueen_US
tuhh.publisher.doi10.5194/ica-proc-4-69-2021-
tuhh.publication.instituteGeovisualisierung, Kartographieen_US
tuhh.type.opusInBuch (Kapitel / Teil einer Monographie)-
tuhh.relation.ispartofseriesnumber4en_US
tuhh.relation.ispartofseriesProceedings of the ICAen_US
tuhh.type.rdmfalse-
openaire.rightsinfo:eu-repo/semantics/openAccessen_US
item.tuhhseriesidProceedings of the ICA-
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
item.fulltextWith Fulltext-
item.creatorOrcidLuft, Jonas Frederik-
item.creatorOrcidSchiewe, Jochen-
item.seriesrefProceedings of the ICA;4-
item.creatorGNDLuft, Jonas Frederik-
item.creatorGNDSchiewe, Jochen-
item.openairetypeinBook-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.languageiso639-1en-
crisitem.author.deptGeovisualisierung, Kartographie-
crisitem.author.deptGeovisualisierung, Kartographie-
crisitem.author.orcid0000-0003-1380-182X-
crisitem.author.orcid0000-0002-6717-0923-
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