Abstract

Earth observation data have proven to be a valuable resource of quantitative information that is more consistent in time and space than traditional land-based surveys. Remote sensing plays a vital role in collecting data in many aspects of life, whether scientific, economic, or political. Land cover information is very important in supporting urban planning and decision-making and provides many opportunities for mapping and monitoring urban areas. Multiple data sources exist, including satellite data of different resolutions ranging from very high to medium resolution and aerial and drone image acquisitions. Today, accurate land cover information is in high demand. Using satellite imagery and remote sensing techniques for planning and development is becoming a common study conducted by many researchers to find practical solutions to the many problems affecting our planet. The recovery, management, and analysis of these large amounts of satellite imagery pose considerable challenges. The classification of satellite images is a very popular and complex topic. In classification studies over the last decade, researchers have been frequently studying only those three machine learning algorithms RF, CART, and SVM, applied in cities or countries except Morocco which poses a great lack of information on the land use of Morocco. To solve these challenges, six machine learning algorithms were applied and compared to each other based on several evaluation metrics, and then, to avoid the problems of data download and storage space, we used Google Earth Engine, a geospatial processing platform that operates in the cloud. It provides free access to substantial satellite data and free computations to monitor, visualize, and analyze environmental features at the petabyte scale. In this paper, we used Landsat 8 satellite data to perform a land cover classification of Morocco, applying machine learning algorithms, which is a subfield of artificial intelligence.