 This article compares the accuracy of six machine learning algorithms for land use slash land cover, LULC, mapping. The algorithms examined are random forest, support vector machine, artificial neural network, fuzzy adaptive resonance theory supervised predictive mapping, spectral angle mapper, and mahalinobus distance. Accuracy was assessed using kappa coefficient, receiver operational curve, index based validation, and root mean square error. Results showed that the random forest algorithm had the highest accuracy of 0.89, while the mahalinobus distance algorithm had the least accuracy of 0.82. The index based LULC and visual cross validation also showed that the random forest algorithm had the highest accuracy level in comparison to the other classifiers. The article concludes that the random forest algorithm is the best machine learning LULC classifier among the six examined algorithms, but further testing is necessary in different morphoclimatic conditions. This article was authored by Swapentalikdar, Pankajsinga, Susanta Mahado, and others. We are article.tv, links in the description below.