Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Kokalj, Žiga"'
Autor:
Jaturapitpornchai, Raveerat, Poggi, Giulio, Sech, Gregory, Kokalj, Ziga, Fiorucci, Marco, Traviglia, Arianna
Publikováno v:
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Deep learning methods in LiDAR-based archaeological research often leverage visualisation techniques derived from Digital Elevation Models to enhance characteristics of archaeological objects present in the images. This paper investigates the impact
Externí odkaz:
http://arxiv.org/abs/2404.05512
Autor:
Sech, Gregory, Soleni, Paolo, der Vaart, Wouter B. Verschoof-van, Kokalj, Žiga, Traviglia, Arianna, Fiorucci, Marco
When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models. The application of transfer learning is frequently employed to mitigate this drawb
Externí odkaz:
http://arxiv.org/abs/2307.03512
The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Di
Externí odkaz:
http://arxiv.org/abs/2208.03163
Publikováno v:
In Geomorphology 15 November 2024 465
Autor:
Kokalj, Žiga, Mast, Johannes
Publikováno v:
In Journal of Archaeological Science: Reports April 2021 36
Autor:
Šprajc, Ivan, Dunning, Nicholas P., Štajdohar, Jasmina, Hernández Gómez, Quintin, López, Israel Chato, Marsetič, Aleš, Ball, Joseph W., Dzul Góngora, Sara, Esparza Olguín, Octavio Q., Flores Esquivel, Atasta, Kokalj, Žiga
Publikováno v:
In Journal of Anthropological Archaeology March 2021 61
Publikováno v:
Journal of Environmental Geography, Vol 11, Iss 3-4, Pp 67-75 (2018)
Drought is a naturally recurring phenomenon of the climate system that affects virtually all regions of the world. During the past decades extreme droughts with extensive negative effects on ecosystems became evident also in the Danube region. At the
Externí odkaz:
https://doaj.org/article/4a91a873acb84490878afc742ce1c7bf
Akademický článek
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Autor:
Soleni, Paolo, der Vaart, Wouter B. Verschoof-van, Kokalj, Žiga, Traviglia, Arianna, Fiorucci, Marco
When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models. The application of transfer learning is frequently employed to mitigate this drawb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b90bc0572c30076e60f86e5b8dd7e78e
http://arxiv.org/abs/2307.03512
http://arxiv.org/abs/2307.03512
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
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