Satellite image search in AgoraEO

dc.contributor.authorAksoy, Ahmet Kerem
dc.contributor.authorDushev, Pavel
dc.contributor.authorZacharatou, Eleni Tzirita
dc.contributor.authorHemsen, Holmer
dc.contributor.authorCharfuelan, Marcela
dc.contributor.authorQuiané-Ruiz, Jorge-Arnulfo
dc.contributor.authorDemir, Begüm
dc.contributor.authorMarkl, Volker
dc.date.accessioned2023-06-27T10:31:44Z
dc.date.available2023-06-27T10:31:44Z
dc.date.issued2022-08-01
dc.description.abstractThe growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes and the limited exploration capabilities provided by EO platforms. To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives. MiLaN is a deep hashing network based on metric learning that encodes high-dimensional image features into compact binary hash codes. We use these codes as keys in a hash table to enable real-time nearest neighbor search and highly accurate retrieval. In this demonstration, we showcase the efficiency of MiLaN by integrating it with EarthQube, a browser and search engine within AgoraEO. EarthQube supports interactive visual exploration and Query-by-Example over satellite image repositories. Demo visitors will interact with EarthQube playing the role of different users that search images in a large-scale remote sensing archive by their semantic content and apply other filters.en
dc.description.sponsorshipEC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarth
dc.description.sponsorshipBMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data
dc.description.sponsorshipBMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data
dc.identifier.issn2150-8097
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/19440
dc.identifier.urihttps://doi.org/10.14279/depositonce-18237
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung; Informatik
dc.subject.othersatellite imagesen
dc.subject.otherAgoraEOen
dc.subject.otherEarth Observationen
dc.subject.otherEOen
dc.subject.othersatellite image archivesen
dc.titleSatellite image search in AgoraEO
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.doi10.14778/3554821.3554865
dcterms.bibliographicCitation.issue12
dcterms.bibliographicCitation.journaltitleProceedings of the VLDB Endowment
dcterms.bibliographicCitation.originalpublishernameAssociation for Computing Machinery
dcterms.bibliographicCitation.originalpublisherplaceNew York, NY
dcterms.bibliographicCitation.pageend3649
dcterms.bibliographicCitation.pagestart3646
dcterms.bibliographicCitation.volume15
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Remote Sensing Image Analysis Group
tub.publisher.universityorinstitutionTechnische Universität Berlin

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