Study of land use/land cover classification using machine learning models: a case study on the Twin Cities of Odisha, India

An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with SVM for Land Use and Land Cover classification. We have used the multispectral Landsat-8 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.