Identification of Art Styles of Tectonic Maps Using Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F24%3A73624666" target="_blank" >RIV/61989592:15310/24:73624666 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-43218-7_70" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-43218-7_70</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-43218-7_70" target="_blank" >10.1007/978-3-031-43218-7_70</a>
Alternative languages
Result language
angličtina
Original language name
Identification of Art Styles of Tectonic Maps Using Machine Learning
Original language description
The paper aims to verify the possibilities of the Orange software for defining and identifying artistic styles of tectonic maps using machine learning techniques. A set of tectonic maps obtained from online sources was tested so that the selection of maps was not influenced by the data capture method. The collected maps differ in color and design mainly. The maps evaluated were included in the analysis of the artistic style in the Orange software as pictures. The Painters embedder was applied, which has the most significant potential for clustering maps according to the artistic style of all embedders in the Orange. All maps were cartographically described and subjected to a “map-use” experiment to obtain a subjective evaluation of artistic style by 30 map readers. The obtained evaluation was compared with the assessment of the artistic style using neural networks from Orange, which was used to determine how maps are grouped according to art map style using already integrated neural networks with hierarchical and non-hierarchical clustering methods. The results of the user evaluation in the experiment were compared with the results of the Orange evaluation. The classification of tectonic maps according to the map style and the results from the Orange embedder were compared. The paper results in a recommendation for the creation of a neural network to evaluate the artistic style of not only tectonic maps. Thanks to the created neural network, searching for maps of an art style identifies easily. The paper reveals an original way of identifying the artistic style of the map to support the interpretation of information in the map.
Czech name
—
Czech description
—
Classification
Type
C - Chapter in a specialist book
CEP classification
—
OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
<a href="/en/project/GA18-05432S" target="_blank" >GA18-05432S: Spatial synthesis based on advanced geocomputation methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology
ISBN
978-3-031-43217-0
Number of pages of the result
4
Pages from-to
299-302
Number of pages of the book
465
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
—