A Comparative Study of Various Deep Learning Approaches to Shape Encoding of Planar Geospatial Objects
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| datacite.relatedItem.issue | 10 | |
| datacite.relatedItem.relatedIdentifierType | ISSN | |
| datacite.relatedItem.relatedItemIdentifier | 2220-9964 | |
| datacite.relatedItem.relationType | IsPublishedIn | |
| datacite.relatedItem.title | IJGI | |
| datacite.relatedItem.volume | 11 | |
| dc.contributor.author | Xiongfeng Yan | |
| dc.contributor.author | Min Yang | |
| dc.date.accessioned | 2025-05-14T14:11:23Z | |
| dc.date.available | 2025-05-14T14:11:23Z | |
| dc.date.issued | 2022 | |
| dc.identifier.doi | https://doi.org/10.3390/ijgi11100527 | |
| dc.identifier.other | jz000147-0521 | |
| dc.identifier.uri | https://tustorage.ulb.tu-darmstadt.de/handle/tustorage/25272 | |
| dc.publisher | MDPI AG | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 550 | |
| dc.title | A Comparative Study of Various Deep Learning Approaches to Shape Encoding of Planar Geospatial Objects | |
| dc.type | Article | |
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