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Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

Casablanca, Morocco's economic capital continues today to fight against the proliferation of informal settle- ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25% of the total slums of Morocco [1]. These are the habitats of all deprived of healthy sanitary conditions and judged precarious from the perspective humanitarian and below the acceptable. The majority of the inhabi- tants of these slums are from the rural exodus with insufficient income to meet the basic needs of daily life. Faced with this situation and to eradicate these habitats, the Moroccan government has launched since 2004 an entire program to create cities without slums (C.W.S.) to resettle or relocate families. Indeed the process control and monitoring of this program requires first identifying and detecting spatial habitats. To achieve these tasks, conventional methods such as information gathering, mapping, use of databases and statistics often have shown their limits and are sometimes outdated. It is within this framework and that of the great German Morocco project “Urban agriculture as an integrative factor of development that fits our project de- tection of slums in Casablanca. The use of satellite imagery, particulary the HSR, has the advantage of providing the physical coverage of urban land but it raises the difficulty of choosing the appropriate method to apply.This paper is actually to develop new approaches based mainly on object-oriented classification of high spatial resolution satellite images for the detection of slums.This approach has been developed for mapping the urban land through by integration of several types of information (spectral, spatial, contextual ...) (Hofmann, P ., 2001, Herold et al. 2002b; Van Der Sande et al., 2003, Benz et al., 2004, Nobrega et al., 2006). In order to refine the result of classification, we applied mathematical morphology and in particular the closing filter. The data from this classification (binary image), which then will be used in a spatial data- base (ArcGIS)

International audienceThis paper presents an integrated method to assess the vulnerability of coastal risks by applying the Fuzzy Analytic Hierarchy Process (FAHP) and spatial analysis techniques with a geographic information system (GIS). The coast of Mohammedia, located in Morocco, was chosen as the study site to implement and validate the proposed framework by applying a GIS-FAHP-based methodology. Coastal risk vulnerability mapping reflects multi-parametric causative factors such as sea level rise, significan twave height, tidal range, shoreline evolution, elevation, geomorphology and distance to an urban area. The results show that the coastline of Mohammedia is characterised by low, moderate and high levels of vulnerability to coastal risk. The high vulnerability areas are situated in the east at Monica and Sablettes beaches. This technical approach helps decision-makers to find optimal strategies and to minimise coastal risks. In comparison with other assessment methods, this approach involves rapid data processing and provides an improved means of sustainable and multi-objective coastal management. Keywords Coastal risk vulnerability, Fuzzy Analytic Hierarchy Process, Digital Shoreline Analysis System, coastal hazard, coastal management

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Mapping lithological units of an area using remote sensing data can be broadly grouped into pixel-based (PBIA), sub-pixel based (SPBIA) and object-based (GEOBIA) image analysis approaches. Since it is not only the datasets adequacy but also the correct classification selection that influences the lithological mapping. This research is intended to analyze and evaluate the efficiency of these three approaches for lithological mapping in semi-arid areas, by using Sentinel-2A data and many algorithms for image enhancement and spectral analysis, in particular two specialized Band Ratio (BR) and the Independent component analysis (ICA), for that reason the Paleozoic Massif of Skhour Rehamna, situated in the western Moroccan Meseta was chosen. In this study, the support vector machine (SVM) that is theoretically more efficient machine learning algorithm (MLA) in geological mapping is used in PBIA and GEOBIA approaches. The evaluation and comparison of the performance of these different methods showed that SVM-GEOBIA approach gives the highest overall classification accuracy (OA≈93%) and kappa coefficient (K) of 0,89, while SPBIA classification showed OA of approximately 89% and kappa coefficient of 0,84, whereas the lithological maps resulted from SVM-PBIA method exhibit salt and pepper noise, with a lower OA of 87% and kappa coefficient of 0,80 comparing them with the other classification approaches. From the results of this comparative study, we can conclude that the SVM-GEOBIA classification approach is the most suitable technique for lithological mapping in semi-arid regions, where outcrops are often inaccessible, which complicates classic cartographic work.

With the rapid rate of population growth and economic development, cities face enormous challenges that require both optimal and integrated solutions to meet the needs of growth and to protect the environment and sustainable development. These urban dynamics, which change over time, extend not only horizontally and upward, but also downward. Thus, underground space has been utilized increasingly to relieve the urban surface and to ensure the exploitation of underground resources. The purpose of this study is to evaluate the possibilities of using this space in Casablanca as part of urban land-use planning and, consequently, to suggest an integrated model of exploitation of this space that is adapted to the specificities of the study area. Thus, an analysis of the use of underground spaces in a set of European cities has been performed. The study of the characteristics of this space in Casablanca has been realized according to the levels of geology and hydrogeology and two underground infrastructure projects. This work has led to the implementation of a prototype model named “Sub-Urban Information Modeling”. The model’s objective is to gather all the data and knowledge related to the relevant underground space in an integrated platform that can be shared and updated in order to facilitate the understanding of this environment and its interaction with the surface and to ensure the rational and efficient use of its resources.