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Abstract. Mobile networks carrier gather and accumulate in their database system a considerable volume of data, that carries geographic information which is crucial for the growth of the company. This work aimed develop a prototype called Spatial On -Line Analytic Processing (SOLAP) to carry out multidimensional analysis and to anticipate the extension of the area of radio antennas.To this end, the researcher started by creating a Data warehouse that allows storing Big Data received from the Radio antennas. Then, doing the OLAP(online analytic processing) in order to perform multidimensional Analysis which used through GIS to represent the Data in different scales in satellite image as a topographic background). As a result, this prototype enables the carriers to receive continuous reports on different scales (Town, city, country) and to identify the BTS that works and performs well or shows the rate of its working (the way behaves) its pitfalls. By the end, it gives a clear image on the future working strategy respecting the urban planning, and the digital terrain model (DTM).

Remote  sensing  has  become  more  and  more  a  reliable  tool  for  mapping  land  cover  and  monitoring  cropland. Much of the work done in this field uses optical remote sensing data. In Morocco, active remote sensing data remain under-exploited despite their importance in monitoring spatial and temporal dynamics of land cover and crops even during cloudy weather. This study aims to explore the potential of C-band Sentinel-1 data in the production of a high-resolution land cover mapping and crop classification within the irrigated Loukkos watershed agricultural landscape in northern Morocco. The work was achieved by using 33 dual-polarized images in vertical-vertical  (VV)  and  vertical-horizontal  (VH)  polarizations.  The  images  were  acquired  in  ascending  orbits  between  April 16 and October 25, 2020, with the purpose to track the backscattering behavior of the main crops and other land  cover  classes  in  the  study  area.  The  results  showed  that  the  backscatter  increased  with  the  phenological  development  of  the  monitored  crops  (rice,  watermelon,  peanuts,  and  winter  crops),  strongly  for  the  VH  and  VV  bands, and slightly for the VH/VV ratio. The other classes (water, built-up, forest, fruit trees, permanent vegetation, greenhouses, and bare lands) did not show significant variation during this period. Based on the backscattering analysis and the field data, a supervised classification was carried out, using the Random Forest Classifier (RF) algorithm.  Results  showed  that  radiometric  characteristics  and  6  days’  time  resolution  covered  by  Sentinel-1  constellation gave a high classification accuracy by dual-polarization with Radar Ratio (VH/VV) or Radar Vegetation Index and textural features (between 74.07% and 75.19%). Accordingly, this study proves that the Sentinel-1 data provide useful information and a high potential for multi-temporal analyses of crop monitoring, and reliable land cover mapping which could be a practical source of information for various purposes in order to undertake food security issues.