Leveraging machine learning and spectroscopic techniques towards estimating paper map scale levels

Category

Published in:

Abstracts of the ICA
Vol. 7.2024 (02.09.2024), art. no. 104, p. 1-2

Volume:

7.2024

Publication date:

02.09.2024

URN:

urn:nbn:de:0307-20251017-006-8

DOI:

10.5194/ica-abs-7-104-2024

Language:

English

Type of Resource:

Text

Extent:

1 Online-Ressource (Seite 1-2, 901,16 kB) : 1 Tabelle, 1 Textdatei (PDF)

Keywords:

map scale; visual heterogeneity; deep learning; paper maps; spectroscopy; Kartenmaßstab; visuelle Heterogenität; Papierkarte; Spektroskopie^GND

DDC subject of DNB:

520 Astronomy & allied sciences

URL:

https://doi.org/10.5194/ica-abs-7-104-2024

URL:

https://ica-abs.copernicus.org/articles/7/104/2024/

Link to Raw Object:

https://ica-abs.copernicus.org/articles/7/104/2024/ica-abs-7-104-2024.pdf

Hosting institution:

University of West Attica (UNIWA) [GR]

Hosting institution:

Internationale Forschungseinrichtungen

Statement of responsibility:

Nikolaos Merlemis, Anastasios Kesidis, Loukas-Moysis Misthos, Vassilios Krassanakis

Bibliographic notes:

Published in: Abstracts of the ICA, Volume 7, 2024 - European Cartographic Conference – EuroCarto 2024; 9–11 September 2024, TU Wien, Vienna, Austria; Editors: Georg Gartner, Florian Ledermann, and Andrea Binn | https://ica-abs.copernicus.org/articles/7/index.html