International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 2, April 2022, pp. 2040~2046 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i2.pp2040-2046  2040 Journal homepage: http://ijece.iaescore.com Land use/land cover classification using machine learning models Subhra Swetanisha 1 , Amiya Ranjan Panda 1 , Dayal Kumar Behera 2 1 School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India 2 Faculty of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India Article Info ABSTRACT Article history: Received Apr 28, 2021 Revised Sep 20, 2021 Accepted Oct 10, 2021 An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers. Keywords: Land use and land cover Machine learning Random forest Remote sensing Support vector machine XGBoost This is an open access article under the CC BY-SA license. Corresponding Author: Dayal Kumar Behera Department of CSE, Silicon Institute of Technology Bhubaneswar, India Email: dayalbehera@gmail.com 1. INTRODUCTION Land use and land cover classification (LULCC) is a critical technique for assessing global change at various spatiotemporal scales [1]. It is a pervasive, accelerating, and substantial process fueled by human activity and frequently results in changes that directly affect humans. The effects of LULCC on ecosystem sustainability are becoming a growing focus of global change study [2]. Till today, there has been a requirement to deliver provincial land use and land cover (LULC) maps and information for a variety of purposes, including change detection [3], planning or monitoring of the urban environment [4], disaster monitoring, landscape planning, resource management, site suitability analysis and ecological studies [5] or biological investigation [6]. Traditionally, non-parametric machine-learning classifiers (ML) such as random forests (RF) and support vector machines (SVMs) [7] have been used for geographical and easy-to-use classification. The focus of this work is to identify the physical aspect of the earth's surface (land cover) as well as how we exploit the land (land use) for the twin cities of Odisha. This can be accomplished by field surveys or through the analysis of satellite pictures (remote sensing) [6]. Conducting field surveys is more thorough and authoritative. It is a costly endeavor that frequently takes a long time to complete. But with recent advancements in the space sector and an increase in the availability of satellite photos (both free and commercial), machine learning models [8] have demonstrated promising outcomes in this field. Recent advancements in sensor technology have resulted in the development of a constellation of satellites [9] and airborne platforms from which a significant amount of spatial resolution remotely sensed imagery is available. Landsat-8 [10] is now circling the earth. The operational land imager sensor (OLI) offers images in