Comparative studies of machine learning classifiers for Land Use Land Cover (LULC) mapping often recommend one algorithm, yet their validation pixels are usually drawn from a few hand-digitised polygons and are far from independent. This study tested whether such differences survive that dependence. Six classifiers, namely Classification and Regression Tree (CART), Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), a multilayer perceptron and a support vector machine (SVM) with a radial basis function kernel, were applied to four top-of-atmosphere bands of a 2020 annual Sentinel-2 median composite of Taipei City, Taiwan, at 10~m. All 110 reference... More >