Comparative Analysis of Modern Methods for Surface Type Identification in RGB Image Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39921880" target="_blank" >RIV/00216275:25410/24:39921880 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001266171500008" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001266171500008</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ZINC61849.2024.10579297" target="_blank" >10.1109/ZINC61849.2024.10579297</a>
Alternative languages
Result language
angličtina
Original language name
Comparative Analysis of Modern Methods for Surface Type Identification in RGB Image Data
Original language description
The rapid development of drone technology and deep learning algorithms also expands the possibilities of environmental monitoring, e.g., the search and management of water bodies. This study aims to harness these advances for the accurate identification of surface types, with a specific focus on water bodies, in RGB image data. Using a dataset comprised of aerial images captured over the Baroch Pond within a nature reserve in the Czech Republic, this study comparative to evaluate the performance of deep learning models, including U-Net, Pyramid Scene Parsing Network (PSPNet), and DeepLabV3, in classifying surface types. The classification accuracy is slightly over 90% for most deep learning algorithms. These results show the potential of deep learning in this area. And this is key for a number of interested parties, for example, state administration, water resource managers, farmers, and tourism industry.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 Zooming Innovation in Consumer Technologies Conference (ZINC)
ISBN
979-8-3503-4916-0
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
84-89
Publisher name
IEEE (Institute of Electrical and Electronics Engineers)
Place of publication
New York
Event location
Novi Sad
Event date
May 22, 2024
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
001266171500008