Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)

<Previous Article In Volume
Next Article In Volume>

Study of Land Use and Land Cover Change Detection Using Machine Learning on GEE of Chandigarh, India

Authors
Amandeep Kaur1, Gurwinder Singh2, *, Amit Jain3, Beena Kapadia4
1Uiversity Institute of Computing, Chandigarh University, Gharuan, India
2Department of Computer Application, Chandigarh School of Business, Division of Research & Innovation, Chandigarh Group of Colleges, Jhanjeri, India
3Department of Computer Application, Chandigarh School of Business, Jhanjeri, India
4KES’ Shroff College, Kandivali West, Mumbai, India
*Corresponding author. Email: gurwindersingh.balahara@gmail.com
Corresponding Author
Gurwinder Singh
Available Online 19 April 2025.
DOI
10.2991/978-94-6463-700-7_4How to use a DOI?
Keywords
Sentinel-2; Machine Learning; Random Forest; Support Vector Machine; Land use; Land cover; Remote Sensing
Abstract

Satellite imagery has proven its skills in the field of evaluating and supervising land use and land cover (LULC) for better eco-friendly management. High-resolution and high-quality datasets can improve LULC classification when implemented with various Machine Learning (ML) and Deep Learning (DL) models. DL requires high-end computation facilities but can give better accuracy results, whereas ML algorithms require learning from humans to make decisions, but its implementation is easy. In this study, the Sentinel-2 satellite imagery dataset is used to study the land statistics of Union Territory, i.e., Chandigarh. The major classes of this study are urban, water bodies, forests, and bare land. In this paper, LULC classification is analyzed using Random Forest (RF). RF model gave an overall accuracy of 96.6%. All the results have proved that RF is delivering the best accuracy among all the other models. This research has a broad spectrum of applications, such as monitoring and mapping land use and land cover areas using ML algorithms.

Copyright
© 2025 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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
Series
Advances in Intelligent Systems Research
Publication Date
19 April 2025
ISBN
978-94-6463-700-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-700-7_4How to use a DOI?
Copyright
© 2025 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.

Cite this article

risenwbib
TY  - CONF
AU  - Amandeep Kaur
AU  - Gurwinder Singh
AU  - Amit Jain
AU  - Beena Kapadia
PY  - 2025
DA  - 2025/04/19
TI  - Study of Land Use and Land Cover Change Detection Using Machine Learning on GEE of Chandigarh, India
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 29
EP  - 35
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_4
DO  - 10.2991/978-94-6463-700-7_4
ID  - Kaur2025
ER  -