Strategies in training deep learning models to extract building from multisource images with small training sample sizes
| dc.contributor.author | Abriha, Dávid | |
| dc.contributor.author | Szabó, Szilárd | |
| dc.date.accessioned | 2023-05-12T17:17:11Z | |
| dc.date.available | 2023-05-12T17:17:11Z | |
| dc.date.issued | 2023 | |
| dc.date.oa | 2024-04-08 | |
| dc.date.pasync | 2023-08-16T23:06:47Z | |
| dc.date.updated | 2023-05-12T17:17:11Z | |
| dc.description.corrector | LB | |
| dc.identifier.citation | International Journal of Digital Earth. -16 : 1 (2023), p. 1707-1724. -Int. J. Digit. Earth. - 1753-8947 | |
| dc.identifier.doi | http://dx.doi.org/10.1080/17538947.2023.2210312 | |
| dc.identifier.issn | 1753-8947 | |
| dc.identifier.opac | https://ebib.lib.unideb.hu/ebib/CorvinaWeb?action=cclfind&resultview=long&ccltext=idno+BIBFORM111611 | |
| dc.identifier.scopus | 85159117341 | |
| dc.identifier.uri | https://hdl.handle.net/2437/353297 | |
| dc.identifier.url | https://www.tandfonline.com/doi/full/10.1080/17538947.2023.2210312 | |
| dc.identifier.wos | 000985898500001 | |
| dc.language | eng | |
| dc.rights.access | open access article | |
| dc.subject.mab | Természettudományok | |
| dc.subject.mab | Földtudományok | |
| dc.tender | Egyéb TKP2020-NKA-04 | |
| dc.tender | Egyéb Kooperatív Doktori Program | |
| dc.tender | Egyéb NKFI K138079 | |
| dc.title | Strategies in training deep learning models to extract building from multisource images with small training sample sizes | |
| dc.type | folyóiratcikk | |
| dc.type | idegen nyelvű folyóiratközlemény külföldi lapban |
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