Fez is the most ancient of the imperial cities of Morocco. In Fez the rate of population growth has been spectacular in recent times (484,300 inhabitants in 1982 and 1,129,768 in 2014). The accelerated rate of population growth has generated a large urban sprawl in all its forms and serious environmental problems. In this research, we have analyzed the relationship between urbanization and land use changes and their impact on cityscape in Fez and the importance of the increase in impervious surface areas. Satellite imageries and census data have been used to identify different patterns of land use change and growth of the city for the period 1984–2013. Classification and analysis of the satellite imageries were performed using Erdas imagine and ArcGIS Software. Urban sprawl in Fez was assessed over 29 years (1984–2013). The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 78% to 87%. The maps showed that between 1984 and 2013 the amount of urban or developed land increased by about 121%, while rural cover by agriculture and forest decreased respectively by 11% and 3%

Lithological mapping is a fundamental undertaking in geological research, mineral resource exploration, and environmental management. However, conventional methods for lithological mapping are often laborious and challenging, particularly in remote or inaccessible areas. Fortunately, a transformative solution has emerged through the integration of remote sensing and machine learning algorithms, providing an efficient and accurate means of deciphering the geological features of the Earth's crust. Remote sensing offers vast and comprehensive data across extensive geographical regions, while machine learning algorithms excel at recognizing intricate patterns and features in the data, enabling the classification of different lithological units. Compared to traditional methods, this approach is faster, more efficient, and remarkably accurate. The combination of remote sensing and machine learning presents numerous advantages, including the ability to amalgamate multiple data sources, provide up-to-date information on rapidly changing regions, and manage vast volumes of data. This review article delves into the invaluable contributions of remote sensing and machine learning algorithms to lithological mapping. It extensively explores diverse remote sensing datasets, such as Landsat, Sentinel-2, ASTER, and Hyperion data, which can be effectively harnessed for this purpose. Additionally, the study investigates a range of machine learning algorithms, including SVM, RF, and ANN, specifically tailored for lithological mapping. By scrutinizing practical use cases, the review underscores the strengths, limitations, and potential future developments of remote sensing and machine learning algorithms in the context of lithological mapping. Practical use cases have demonstrated the immense potential of machine learning algorithms, with the SVM classifier emerging as a reliable and accurate option for lithological mapping. Moreover, the choice of the most appropriate data source depends on the specific objectives of the application.

There is an increasing demand for 3D city models for many applications and users worldwide. Fez is one of the most important tourism locations in Morocco and is a challenge for 3D city modeling due to its complex buildings and road structure. Due to its importance this Historic Medina of Fez was added to the UNESCO World Heritage List in 1981. It is located in the northern part of Morocco. In this work, we discuss the construction of a digital surface model based on aerial image stereo pairs using matching method and the use of this DSM for 3D city planning. We used aerial photographs with high accuracy (1/4000) covering the study area acquired in 2007 and additional cartographic data from Fez. 3D land use zoning allowed building volumes, usage, and density. They are the main tools defining the image of a city and bring into focus the model of best practice of the rehabilitation and conservation of the historic Medina