Enhanced Feature-Based Clustering for Urban Land Use Pattern Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73631307" target="_blank" >RIV/61989592:15310/25:73631307 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/KSE63888.2024.11063614" target="_blank" >http://dx.doi.org/10.1109/KSE63888.2024.11063614</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/KSE63888.2024.11063614" target="_blank" >10.1109/KSE63888.2024.11063614</a>
Alternative languages
Result language
angličtina
Original language name
Enhanced Feature-Based Clustering for Urban Land Use Pattern Detection
Original language description
Urban land use patterns are essential for urban planning and environmental management. Traditional analysis methods, such as manual surveys and satellite imagery interpretation, are often labor-intensive and struggle with large-scale data. In contrast, data-driven approaches offer promising alternatives. This paper addresses the challenge of detecting urban land use patterns by proposing an efficient framework that leverages the Vision Transformer (ViT) and Variational Autoencoder (VAE) models to generate robust feature representations from images. To enhance the clustering process, UMAP (Uniform Manifold Approximation and Projection) is used for dimensionality reduction, followed by k -means clustering. Our approach overcomes the limitations of existing image clustering tools by generating embedding features that capture both high-level semantic information and underlying generative structure. Comparative experiments were conducted on the real-life Urban Atlas dataset to evaluate the performance of the proposed framework against various feature extraction options in the proposed methods. Experimental results show that the proposed method outperforms others by capturing the complexities of urban land use patterns, offering enhanced flexibility and robustness.
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference on Knowledge and System Engineering
ISBN
979-8-3315-0940-8
ISSN
2164-2508
e-ISSN
2694-4804
Number of pages
6
Pages from-to
78-83
Publisher name
IEEE Computer Society Press
Place of publication
New York
Event location
Kuala Lumpur, Malajsie
Event date
Nov 5, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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