Machine Learning Based Decadal Land Use Land Cover Analysis of Karnataka

DOI

10.2991/978-94-6239-713-2_24How to use a DOI?

Keywords

LULC; Ensemble Machine Learning; Support Vector Machine; Satellite Imagery; GIS

Abstract

The use of machine learning algorithms to analyze land use and land cover (LULC) change has become more important for environmental monitoring and urban planning. This paper looks at how well ensemble machine learning models can map and quantify land transformation patterns across Karnataka State over four decades, from 1994 to 2024. The study uses Support Vector Machine (SVM) and Random Forest (RF) algorithms along with Geographic Information System (GIS) techniques to improve the accuracy of spatial quantification and classification. Multi-temporal satellite images were analyzed to observe changes between urban and non-urban areas, as well as shifts in vegetation, forests, and agricultural land. The results show a significant rise in built-up areas from 21.76 sq.km in 1994 to 48.25 sq.km in 2024, along with a slow decline in vegetation and forest cover. The ensemble model proved to be more accurate than individual classifiers, offering a better representation of land cover distribution. These findings showes how effective it is to combine machine learning with geospatial tools for sustainable land management and long-term environmental assessment.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pamarthi Chennarao
AU  - Pandala Madhavi Latha
AU  - Kesari Bhavani Prasad Reddy
AU  - Veeramallu Satya Sahithi
PY  - 2026
DA  - 2026/06/25
TI  - Machine Learning Based Decadal Land Use Land Cover Analysis of Karnataka
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 326
EP  - 335
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_24
DO  - 10.2991/978-94-6239-713-2_24
ID  - Chennarao2026
ER  -