Using of Open Access remote sensing Data in Google earth engine platform for mapping built-up area in Marrakech City, Morocco

The United Nations has been estimated that two-thirds of the Moroccan population will live in the cities in 2020, which makes the cities face major challenges, such as putting pressure on infrastructure, health, education, and the environment. In order to avoid these challenges and turn them into gains, it is necessary to put the perfect plans by the policymakers. For this reason, it required to mapping the land cover land use (LC/LU) to measure and map built-up expansion in the cites. This study aims to provide a more effective tool for mapping built-up and non-built areas in the city of Marrakesh in Morocco and compare these results with Atlas of urban expansion project. We used both 8069 polygons from cadastral administrative plans and 9269 control points as input sources, 80% of the data as training data and 20% for the validation, three supervised machine learning classifier algorithms have been used; random forest (RF), support vector machine (SVM), and CART. The results showed that the link between satellite imagery and local cadastral administrative data is more effective, especially in the empty lands in the center and around the city. The overall accuracy and Kappa indicators showed that RF is more powerful than SVM, and CART for mapping urban areas.