Deep Learning in Historical Geography
Loading...
Date
2024-05-07
Authors
Vynikal, Jakub
Pacina, Jan
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Vysoké učení technické v Brně,Fakulta stavební
Altmetrics
Abstract
In relation to the rapid development of artificial intelligence, the possibilities of automatic processing of spatial data are increasing. Scanned topographical maps are a valued source of historical information. Neural networks allow us to extract information quickly and efficiently from such data, eliminating the difficult and repetitive work that would otherwise have to be done by a human. The article presents two case studies exploring the possibilities of using deep learning in historical geography. The first one is concerned with detecting and extracting swamps from topographic maps, while the second one attempts to automatically vectorize contours from the State Map 1 : 5 000
Description
Citation
Juniorstav 2024: Proceedings 26th International Scientific Conference Of Civil Engineering, s. 1-5. ISBN 978-80-86433-83-7.
https://juniorstav.fce.vutbr.cz/proceedings2024/
https://juniorstav.fce.vutbr.cz/proceedings2024/
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
© Vysoké učení technické v Brně,Fakulta stavební