COMPARISON OF MACHINE LEARNING CLASSIFIERS FOR MULTITEMPORAL AND MULTISENSOR MAPPING OF URBAN LULC FEATURES
This study compares four machine-learning algorithms comprising of Classification And Regression knesko coupon code Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB) and Support Vector Machine (SVM) for the classification of urban land-use and land-cover (LULC) features.Using multitemporal and multisensor Landsat data from 1984-2020 at