An efficient fusion-based deep learning framework for land use and land cover image clustering

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73631713" target="_blank" >RIV/61989592:15310/25:73631713 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S095219762502069X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S095219762502069X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2025.112061" target="_blank" >10.1016/j.engappai.2025.112061</a>

Result language

angličtina

Original language name

An efficient fusion-based deep learning framework for land use and land cover image clustering

Original language description

Land use and land cover (LULC) analysis is vital for understanding spatial dynamics and informing environmental management, urban planning, and sustainable development. Traditional approaches, such as manual surveys and conventional image clustering methods, often face limitations in scalability and adaptability. This paper presents a novel deep learning framework that combines the Vision Transformer (ViT) and Variational Autoencoder (VAE) to extract complementary feature representations for LULC image clustering. The ViT tokenizes image patches to capture high-level semantic features, while the VAE models latent structures to integrate contextual and structural information. To further improve clustering performance, the framework incorporates Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction followed by k-means++ clustering, enabling a scalable and robust solution for diverse datasets. Experiments on multiple datasets, including the Urban Atlas LULC 2018 dataset and recent LULC maps of Japan and Vietnam, demonstrate the framework&apos;s superior ability to capture complex LULC patterns compared to traditional methods. The datasets and source code will be made publicly available at https://github.com/ClarkDinh/LULCMiner. This framework has broad applications across geospatial and remote sensing engineering, civil and environmental engineering, agricultural planning, transportation, and urban development

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

ISSN

0952-1976

e-ISSN

1873-6769

Volume of the periodical

161

Issue of the periodical within the volume

DEC

Country of publishing house

GB - UNITED KINGDOM

Number of pages

23

Pages from-to

"112061-1"-"112061-23"

UT code for WoS article

001569066600001

EID of the result in the Scopus database

2-s2.0-105015151224