Apatite fission track dating from a central transect in the Argentera massif (southernmost External Crystalline Massif = ECM) yielded ages between 8.05 + 0.6 and 2.4 + 0.2 Myr, with a positive age/altitude correlation above 3 Ma, 1200 m. Recognising a thermal peak at c. 2508C, 33 Ma, based on stratigraphic, metamorphic and 39 Ar/ 40 Ar data, the present results suggest a slow cooling rate (8±58C) for the Argentera massif during the Oligocene±early Pliocene. This rate compares with that from the Pelvoux massif, but contrasts with those observed in the northern ECM (Mont-Blanc and Aar: up to 148C Myr 71 ) for the same time interval. This can be related to the different location of the ECM within the collided European margin. At about 3±4 Ma, the denudation rate would have increased up to c. 1 mm yr 71 in the Argentera massif, reaching the same value as in the Belledonne and northern ECM, likely a consequence of Penninic thrust inversion.

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Determining an impervious surface is one of the most important topics of remote sensing because of its great role in providing information that benefits decision-makers in urban planning, sustainable development goals, and environmental protection. In recent years, a great development in this field has occurred due to the huge improvement in the algorithms and techniques that are used to map impervious surfaces. In this paper, the deep learning technique has been implemented to investigate the extraction of impervious surfaces in Marrakesh city, based on Landsat images. 9000 polygons and 13840 points have been taken to prepare label data by random forest in Google Earth Engine (GEE). In addition, all preprocessing steps for remote sensing images have been implemented in GEE. An artificial neural network (ANN) has been used to determine impervious surfaces. After training and testing the proposed network on Landsat image datasets, precision, accuracy, recall, and F1-score matrix scores were 0.79, 0.98, 0.87, and 0.82, respectively. The experimental results show that this method is efficient and precise for mapping the impervious surfaces of Marrakesh city.

Abstract. In recent years, deep convolutional neural networks (CNNs) algorithms have demonstrated outstanding performance in a wide range of remote sensing applications, including image classification, image detection, and image segmentation. Urban development, as defined by urban expansion, mapping impervious surfaces, and built-up areas, is one of these fascinating issues. The goal of this research is to explore at and summarize the deep learning approaches used in urbanization. In addition, several of these methods are highlighted in order to provide a comprehensive overview and comprehension of them, as well as their pros and downsides.