A Comparative Study of Various Deep Learning Approaches to Shape Encoding of Planar Geospatial Objects

datacite.relatedItem.firstPage527
datacite.relatedItem.issue10
datacite.relatedItem.relatedIdentifierTypeISSN
datacite.relatedItem.relatedItemIdentifier2220-9964
datacite.relatedItem.relationTypeIsPublishedIn
datacite.relatedItem.titleIJGI
datacite.relatedItem.volume11
dc.contributor.authorXiongfeng Yan
dc.contributor.authorMin Yang
dc.date.accessioned2025-05-14T14:11:23Z
dc.date.available2025-05-14T14:11:23Z
dc.date.issued2022
dc.identifier.doihttps://doi.org/10.3390/ijgi11100527
dc.identifier.otherjz000147-0521
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/25272
dc.publisherMDPI AG
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550
dc.titleA Comparative Study of Various Deep Learning Approaches to Shape Encoding of Planar Geospatial Objects
dc.typeArticle
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