Change Detection of LULC using Machine Learning
- https://doi.org/10.2991/ahis.k.210913.042How to use a DOI?
- Accuracy, Change Detection, Classification, LULC, Sentinel 2
This paper discusses detection of change in land usage in Davangere (Karnataka State, India) between the years 2016 and 2021. After the place has been declared as one of the smart cities identified by the Govt. of India in 2014 and subsequent to the international price crash for sugar, there were noticeable changes in land utilization in terms of urbanization and shift in traditional cropping pattern. The objective of this research work is to capture this change using remote sensing, the images from MSI Sentinel-2 were collected at two points of time and processed for LULC with the help of supervised machine learning classifiers such as Minimum Distance, Mahalanobis Distance and Maximum Likelihood to ascertain the accurate one. It was found that Maximum Likelihood classifier ensures highest accuracy of 95.2%. It was also found that during the study period, there was a significant change in the land use with respect to Built-up area and Area under cultivation of Paddy.
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - M Geetha AU - Karegowda Asha Gowda AU - R Nandeesha AU - B V Nagaraj PY - 2021 DA - 2021/09/13 TI - Change Detection of LULC using Machine Learning BT - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021) PB - Atlantis Press SP - 339 EP - 347 SN - 2589-4900 UR - https://doi.org/10.2991/ahis.k.210913.042 DO - https://doi.org/10.2991/ahis.k.210913.042 ID - Geetha2021 ER -