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This study investigates the possibilities of improving the classification of high spatial resolution images by using object-based approach, superpixel segmentation and compact texture unit descriptors. The proposed approach was implemented on Google Earth Engine (GEE) which provides a fast and easy-to-use platform with its freely available datasets and geospatial analysis tools for applications such multi-class classification. In this work, Multispectral Instrument (MSI) images of Sentinel-2 were utilized to classify main land-cover and land-use types. The obtained results were validated using the Corine land cover inventory.