Abstract- Land cover represent characteristic of earth surface. By utilizing the abundance of geotagged images from online crowdsource images like geotagged photo library (https://eomf.ou.edu/photos) from the University of Oklahoma, prediction of land cover types will be established by using machine learning techniqus. RGB Histogram, edge orientation and vegetation indices were used to obtain 8 features that representing images, therefore several classifiers were performed to observe which of classifiers produce best accuracy. Best classifier produces 82% in overall validation accuracy and 89% of 74 unclassified images was successfully predicted images were mapped into geographic information system (GIS) to show land cover in GIS. This model was measured by using precision, recall, F-Test and kappa coefficient. The performance of each measurement reaches 89.8%, 88.1%, 88.6%, 85.6%