Exploring the Latent Manifold of City Patterns

dc.contributor.authorAgoub, Amgad
dc.contributor.authorKada, Martin
dc.date.accessioned2021-11-08T12:40:38Z
dc.date.available2021-11-08T12:40:38Z
dc.date.issued2021-10-11
dc.date.updated2021-11-05T00:24:50Z
dc.description.abstractUnderstanding how cities evolve through time and how humans interact with their surroundings is a complex but essential task that is necessary for designing better urban environments. Recent developments in artificial intelligence can give researchers and city developers powerful tools, and through their usage, new insights can be gained on this issue. Discovering a high-level structure in a set of observations within a low-dimensional manifold is a common strategy used when applying machine learning techniques to tackle several problems while finding a projection from and onto the underlying data distribution. This so-called latent manifold can be used in many applications such as clustering, data visualization, sampling, density estimation, and unsupervised learning. Moreover, data of city patterns has some particularities, such as having superimposed or natural patterns that correspond to those of the depicted locations. In this research, multiple manifolds are explored and derived from city pattern images. A set of quantitative and qualitative tests are proposed to examine the quality of these manifolds. In addition, to demonstrate these tests, a novel specialized dataset of city patterns of multiple locations is created, with the dataset capturing a set of recognizable superimposed patterns.en
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlinen
dc.identifier.eissn2220-9964
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13832
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-12608
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc550 Geowissenschaftende
dc.subject.othercity patternsen
dc.subject.otherdimensionality reductionen
dc.subject.otherurban planningen
dc.subject.otherdeep learningen
dc.titleExploring the Latent Manifold of City Patternsen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber683en
dcterms.bibliographicCitation.doi10.3390/ijgi10100683en
dcterms.bibliographicCitation.issue10en
dcterms.bibliographicCitation.journaltitleISPRS International Journal of Geo-Informationen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume10en
tub.accessrights.dnbfreeen
tub.affiliationFak. 6 Planen Bauen Umwelt::Inst. Geodäsie und Geoinformationstechnik::FG Methodik der Geoinformationstechnikde
tub.affiliation.facultyFak. 6 Planen Bauen Umweltde
tub.affiliation.groupFG Methodik der Geoinformationstechnikde
tub.affiliation.instituteInst. Geodäsie und Geoinformationstechnikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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