Assessing Machine Learning Algorithms for Land Use and Land Cover Classification in Morocco Using Google Earth Engine

Google Earth Engine constitutes a cloud-based geospatial data processing platform. It grants free access to vast volumes of satellite data along with unlimited computational power, enabling the monitoring, visualization, and analysis of environmental features on a petabyte scale. The platform's capacity to accommodate various land use and land cover (LULC) classification approaches, utilizing both pixel-based and object-oriented methods, has been facilitated by providing an array of machine learning algorithms. Earth observation data has emerged as a valuable resource, offering temporally and spatially consistent quantitative information compared to traditional ground surveys. It presents numerous opportunities for urban mapping, monitoring, and a wide array of physical, climatic, and socio-economic data to support urban planning and decision-making. In this study, Landsat 8 satellite data was harnessed for supervised classification. Three advanced machine learning techniques-Support Vector Machine (SVM), Random Forest (RF), and Minimum Distance (MD)-were employed to categorize areas within Morocco, encompassingwater bodies, built-up regions, cultivated land, sandy areas, barren zones, and forests. The classification outcomes are presented using a set of accuracy indicators, including Overall Accuracy (OA) and the Kappa coefficient.