MACHINE LEARNING–BASED LULC CLASSIFICATION IN THE ASAKA DISTRICT (1995–2025)

Authors

  • Hayitova Maqsuda Rafiq kizi, Ro’ziqulova Oyxumor Shermamatovna, National Research University “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers”
  • Zikirov Orifjon Olimjonovich “Inspection on the control of agro-industrial complex under the Cabinet of Ministers of the Republic of Uzbekistan”

DOI:

https://doi.org/10.55640/

Keywords:

Landsat 5, Landsat 8, classification, SVM, remote sensing, LULC.

Abstract

In this study, Landsat 5 data from 1995 and Landsat 8 data from 2025 were used, with both datasets acquired in May. A supervised machine learning approach based on the Support Vector Machine (SVM) algorithm was applied to classify land use and land cover (LULC) in the Asaka District. As a result, LULC maps and land use change maps of the study area were generated.

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References

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Published

2025-12-22 — Updated on 2025-12-22

Versions

Issue

Vol. 4 No. 11 (2025): Journal of Multidisciplinary Sciences and Innovations

Section

Articles

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Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.

How to Cite

MACHINE LEARNING–BASED LULC CLASSIFICATION IN THE ASAKA DISTRICT (1995–2025). (2025). Journal of Multidisciplinary Sciences and Innovations, 4(11), 2675-2680. https://doi.org/10.55640/

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