A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder

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datacite.relatedItem.firstPage456
datacite.relatedItem.issue7
datacite.relatedItem.relatedIdentifierTypeISSN
datacite.relatedItem.relatedItemIdentifier2220-9964
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datacite.relatedItem.titleIJGI
datacite.relatedItem.volume9
dc.contributor.authorFatemeh Noori
dc.contributor.authorHamid Kamangir
dc.contributor.authorScott A. King
dc.contributor.authorAlaa Sheta
dc.contributor.authorMohammad Pashaei
dc.contributor.authorAbbas SheikhMohammadZadeh
dc.date.accessioned2025-05-14T16:30:30Z
dc.date.available2025-05-14T16:30:30Z
dc.date.issued2020
dc.identifier.doihttps://doi.org/10.3390/ijgi9070456
dc.identifier.otherjz000157-0452
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/31416
dc.publisherMDPI AG
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550
dc.titleA Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder
dc.typeArticle
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