Moroccan oases, vital ecosystems in arid and semi-arid regions, are increasingly vulnerable to the impacts of drought, which severely affect vegetation health and elevate wildfire risks. This study investigates the relationship between drought severity, oasis vegetation vitality, and fire risk across various Moroccan oases. Using satellite remote sensing data, we developed the Aggregated Wildfire Hazard Index (AWHI), which combines the Watershed Integrated Multi-Drought Index (WIMDI), the Vegetation Health Index (VHI), and the Normalized Burn Ratio (NBR), through Locally Optimized Weighted Aggregation (LOWA), to assess the compounded effects of drought and vegetation stress on fire susceptibility. The results demonstrate a strong inverse correlation between drought intensity and vegetation health, with heightened drought conditions significantly increasing wildfire risk due to accumulated dry biomass and reduced moisture content. Given the particularly high fire risk in the Aoufous oasis, followed by Erfoud-Errissani and Goulmima, the study emphasizes the strategic deployment of IoT-based fire surveillance systems within a wildfire digital twin platform. Our findings highlight the urgent need for adaptive climate resilience measures and advanced fire monitoring strategies to protect these fragile ecosystems from the growing threats of climate change.

The advent of digital transformation has redefined the preservation of cultural heritage and historic sites through the integration of Digital Twin technology. Initially developed for industrial applications, Digital Twins are now increasingly employed in heritage conservation as dynamic, digital replicas of physical assets and environments. These systems enable detailed, interactive approaches to documentation, management, and preservation. This paper presents a detailed framework for implementing Digital Twin technology in the management of heritage buildings. By utilizing advanced methods for data collection, processing, and analysis, the framework creates a robust data hub for Digital Twin Heritage Buildings (DTHB). This architecture enhances real-time monitoring, improves accuracy, reduces operational costs, and enables predictive maintenance while minimizing invasive inspections. Focusing on Bab Al-Mansour Gate in Meknes, Morocco, a significant cultural landmark, this research outlines the workflow for developing a Bab Al-Mansour DTHB platform. The platform monitors structural health and detects damage over time, offering a dynamic tool for conservation planning. By integrating innovative technologies with data-driven solutions, this study provides a replicable model for preserving heritage sites, addressing critical gaps in real-time monitoring, resource optimization, and environmental risk mitigation.

This paper shows the efficiency of machine learning for improving land use/cover classification from synthetic aperture radar (SAR) satellite imagery as a tool that can be used in some sub-Saharan countries that experience frequent clouds. Indeed, we aimed to map the land use and land cover, especially in agricultural areas, using SAR C-band Sentinel-1 (S-1) time-series data over our study area, located in the Kaffrine region of Senegal. We assessed the performance and the processing time of three machine-learning classifiers applied on two inputs. In fact, we applied the random forest (RF), K-D tree K-nearest neighbor (KDtKNN), and maximum likelihood (MLL) classifiers using two separate inputs, namely a set of monthly S-1 time-series data acquired during 2020 and the principal components (PCs) of the time-series dataset. In addition, the RF and KDtKNN classifiers were processed using different tree numbers for RF (10, 15, 50, and 100) and different neighbor numbers for KDtKNN (5, 10, and 15). The retrieved land cover classes included water, shrubs and scrubs, trees, bare soil, built-up areas, and cropland. The RF classification using the S-1 time-series data gave the best performance in terms of accuracy (overall accuracy = 0.84, kappa = 0.73) with 50 trees. However, the processing time was relatively slower compared to KDtKNN, which also gave a good accuracy (overall accuracy = 0.82, kappa = 0.68). Our results were compared to the FROM-GLC, ESRI, and ESA world cover maps and showed significant improvements in some land use and land cover classes.

The city of Eljebha, Morocco, and its surroundings have always been affected by instability and slope failure, both natural and man-made. The Neogene conglomeratic clay formations, which form most of the city of Constantine, are extremely sensitive to the presence of water, which makes them susceptible to landslides. Therefore, to preserve the environment and endangered species, remote sensing has been designed to facilitate the monitoring and supervision of natural hazards and threats. The present paper deals with the detection of stable points by the new technique of permanent dispersers "PSinsar" as well as the subsidence rate with an accuracy in mm/year in the area of El Jebha in Morocco from 2016 to 2018 using Sentinel 1 complex SLC data in IW mode

In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.

Remotely sensed data has become an effective, operative and applicable tool that provide critical support for geological surveys and studies by reducing the costs and increasing the precision. Advances in remote-sensing data analysis methods, like machine learning algorithms, allow for easy and impartial geological mapping. This study aims to carry out a rigorous comparison of the performance of three supervised classification methods: Random Forest, k-Nearest Neighbor and maximum likelihood using remote sensing data and additional information in Souk El Had N’Befourna region. The enhancement of remote sensing geological classification by using geomorphometric features, principal component analysis, gray level co-occurrence matrix (GLCM) and multispectral data of the Sentinel-2A imagery was highlighted. The Random Forest algorithm showed reliable results and discriminated limestone, dolomite, conglomerate, sandstone and rhyolite, silt and Alluvium, ignimbrite, granodiorite, Lutite, granite, and quartzite. The best overall accuracy (~91%) was achieved by Random Forest algorithm.

Accurate and reliable lithological mapping through satellite-borne remote sensing data and image classification approaches has a critical role since it can automatically and promptly identify lithological units over large areas. Most available Pixel-Object Based comparative classification studies have been applied to land use land cover (LULC) studies; however, this research aims to evaluate and compare the performance of these digital classification methods in the field of geological mapping in semi-arid areas, by integrating spectral bands and neo-bands, particularly the Minimum noise fraction (MNF) and the principal component analysis (PCA), of Sentinel-2A satellite imagery, to map the southern of Skhour Rehamna which is located at the western Moroccan Meseta. The analysis results from two different methods, namely, pixel-based image analysis (PBIA) with k-nearest neighbour (K-NN) and Random Forest (RF) machine learning algorithms (MLAs), and Geographic Object-Based Image Analysis (GEOBIA) were assessed and compared. PBIA method involved selection of training areas whether it was k-NN or RF MLAs, and produced lithological maps that exhibit "salt and pepper" effects as well as problems associated to delineating accurate lithological boundaries, while GEOBIA approach involved multi-resolution segmentation step where scale, shape and compactness parameters should be adjusted as accurate as possible, in order to segment the image into homogeneous and meaningful regions so that the resulted samples were classified using Standard Nearest Neighbour algorithm. Therefore, the resulting lithological maps were assessed by comparing both techniques using confusion matrix, overall accuracy (OA) and Kappa coefficient (K). The results show that the GEOBIA approach had higher overall agreement (83.46% OA and 0.76 K) than RF (81.92% OA and 0.72 K) and k-NN (80.79% OA and 0.70 K) PBIA approaches. Overall, the results clearly indicate the potential of GEOBIA technique for lithological mapping applications to produce more realistic maps.

From the years 2000, the idea of Smart Cities became the new tendency around the world; cities like Barcelona, Nice, and Singapore based their management on digitized data. Attracted by this new global tendency Moroccan emerging country launched the Casablanca Smart City project. Considered as the economic capital with 30% of the national GDP and the most populous city in the country, Casablanca has the potential to become a Smart City and lead the other African cities in the same path. However, the project is facing tremendous challenges; the most concerning ones are Rapid Urbanization and Population growth. With 3.4 million people in the urban area and more than 4.3 million in its agglomeration, Casablanca has experienced an unprecedented population growth in the last decades [1]. The urban growth and sprawl, added to the failing strategies to control them have resulted in an environmental and urban crisis. GIS and Remote Sensing are key technologies for the decision makers to help achieve sustainable development and raise Casablanca to the level of a Smart City. This paper addresses the smart city challenges of Casablanca with regards to Urban Growth and Sprawl, Built-up Density, and Urbanization. The following analysis is one of the few to raise those aspects in Morocco.

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 $\approx ~93$ %) 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.

Tensift region is known by an arid and semi-arid climate. The city of Marrakech, experience an important temporal variability in rainfall and an increasing trend of temperature during the last five decades. However, the evolution of rainfall is more contrasted by a downward trend in Marrakech, it is also distinguished by alarming clues of global warming, like the number of hot days and heat waves. This paper evaluates the effect of climate change on Vegetation cover (evaluated by NDVI) in the Tensift region, Morocco. Temperature, rainfall data, and vegetation cover were used in this analysis. The effect of climate change on vegetation was studied using a linear regression analysis method, to explore the relationship between vegetation dynamics and climate factors (the trend of vegetation dynamics for the years between 1973 -2019). Analysis of annual data, for the temperature and vegetation cover shows a low regression coefficient value of 0.3057 and, rainfall and vegetation cover analysis show regression coefficient value of 0.7024. Monthly periods analysis showed that the most important period is the one between December and May where the regression coefficient value is 0.6568. In more details the period between March and May shows regression coefficient value of 0.4685, the period between December and February shows regression coefficient value of 0.434, the period between September and November shows regression coefficient value of 0.0734. The study reveals that, rainfall is significantly related with vegetation cover, especially the period between December and May, more than the temperature does in Tensift region.

Satellite-borne remote sensing images are considered as one of the most important data sources for lithological mapping due to their extensive geographical coverage at an efficient cost. Integration of optical along with microwave satellite datasets, are leading to increase the lithological mapping accuracy. In this study, Geographic Object-Based Image Analysis (GEOBIA) was applied, using data from the freely available European Space Agency (ESA) Sentinel 1 SAR data and Sentinel 2 optical imagery, that were fused With a digital elevation model (DEM) of 13m spatial resolution, generated from two single look complex (SLC) sentinel 1 (C band) interferometry, in conjunction with slope and two geomorphic indices, Terrain Ruggedness (TRI) and Terrain Position Index (TPI) for mapping lithology in the southern of the Palaeozoic massif of Skhour Rehamna in Morocco. The statistical results of the fusion of Sentinel 1 and Sentinel 2 datasets have shown the highest accuracies, showing an overall accuracy (OA) of 92.80% and a kappa coefficient of and 0.89 compared to the layer stack of Sentinel 2 image bands with the first three Minimum noise fraction (MNF) and the Principal components bands (PC1, PC2 and PC6) that showed an OA of 91.50% and a kappa coefficient of and 0.87. With the achievable results in this study, the technique is useful in discriminating general rock type that outcrop in semi-arid regions.

the environmental data acquisition station is used to manage the public health and detection of a microclimate of a city for a broadcast processed high precision data for users. This research article is summarizing the applications of Electronics and GIS applications to health risks management due to air pollution, noise and microclimate change detection, to study the geography of urban health risks and environmental change in Casablanca, Morocco. acquisition of environmental and Climatological Data using sensor networks, results a new possibilities and advantages, of environmental data monitoring using electronic circuit with a broadcasting system via a technology with wireless transmitter. The main object of this research is to present a simple and rapid method to provide instant microclimate and environment data, which can be used to manage the public risks caused by pollution and environment changes. As the climate and environment conditions are variable from a place to place, it is difficult to get accurate climate and environment for a particular location in a city. With the advancement of technology, especially data acquisition systems, the problem of large set up area and cost has been reduced. The data acquisition station can be set up at any place and provide an accurate and instant climate and environment report to any citizen of the city. The report of the data acquisition system can be used by the users to choose the best place for them to live due to their diseases. In addition, these data can be collected in a GIS system to create a microclimate change and air pollution map of different zones in the city, predict the future climate, and air pollution of the city.

Geological mapping plays a very important role in the exploration of oil, mineral and water resources, as well as in identifying and monitoring natural hazards. It is an indispensable means for the economic development of the country. Remote sensing data provides critical support by reducing the costs and increasing the precision. This research work evaluates the use of Random Forests, a supervised machine learning algorithm, for geological mapping of the Msaidira-Souk Al Had region, a part of the sidi Ifni inlier situated in southern Morocco. By integrating the spectral and textural features of Sentinel-2A with the morphometric attributes of Digital Elevation Model (DEM) of ALOS/PALSAR. The experiment revealed that the overall accuracy reaches ≈ 91% while the kappa coefficient is 88%. As the final result of this research, the Random Forest method is an effective tool that geoscientists can use to produce a new map or to update existing geological maps.

An extensive range of remote sensing-based approaches has been used to lithological mapping in conjunction with multispectral satellite imagery. Thus, this study focuses on mapping and comparing thematic classification methods of lithologies using Sentinel 2 imagery in the southern of Skhour Rehamna. First, Pixel-Based Image analysis (PBIA) techniques were evaluated, particularly, RF and K-NN machine learning algorithms (MLAs), both methods generalized lithological maps that is characterized with “salt and pepper” noise of misclassified pixels and poorly defined boundaries. The second approach is Geographic ObjectBased Image Analysis (GEOBIA), which involved multiresolution segmentation part based on several predefined scale, shape and compactness parameters that must be subjected to relative weighting in order segment the image to homogeneous areas, whilst, standard Nearest Neighbor classification was applied to the given samples. Then, classifications accuracies were assessed by comparing both methods using confusion matrix and Kappa coefficient. The results show that the GEOBIA approach provides the best mapping results with an overall accuracy of approximately 83.46% and Kappa coefficient of 0.76 compared with the results obtained by PBIA approach, with 81.92% and 80.79%, and Kappa coefficient of 0.72 and 0.72, for RF and K-NN respectively. As a conclusion, the results clearly indicate the potential of GEOBIA technique for lithological mapping.

Land surface displacement caused by landslides is among the most damaging phenomena in northern Morocco. In this paper, we measure ground deformation in the Chefchaouen area which is a zone characterized by geological formations and structurally complex losses that promote instability (landslide, mudflow, block falls, etc.) leading to slow to extremely slow deformation phenomena, which require an interferometric study, using the DinSAR (differential interferometric synthetic Aperture Radar) technique with sentinel 1 images in bance C, which is a powerful tool for the detection and analysis of interferences and monitoring of ground deformations. We worked on four areas of the study area. Its points are provided by the direction of the roads, which generates Interferograms and then deformation maps with precision in mm/year.

Urbanization of rural areas and rural exodus are the main factors characterizing the demographic evolution in Morocco. Moroccan cities are expected to host 73.7% of the country’s inhabitants by 2050 instead of 60.4% in 2014 that is 32.1 million and 20.4 million respectively. According to the 2014 National population census, Casablanca’s population growth has been impressive in recent years with an estimated population of 3.35 million in the urban area and over 4.27 million on the outskirts. In fact, the city of Casablanca is experiencing an urban and environmental crisis as result of the unprecedented fast urban and demographic growth in the last decades, compiled with chronic deficiency of integrative urban strategies. Urban planners have used remote sensing and GIS as essential tools to ensure better living quality and sustainable development. This study is the first of its kind in Morocco. In this paper, we have analysed the transformation of the city of Casablanca in terms of built-up density, urban growth and sprawl and the connection between land use cover changes and urbanization and their impact on the city shape. Urban growth and sprawl in Casablanca have been assessed over a period of 33 years (1986-2019). Census data and Satellite imageries have been utilized to accomplish this study, using classification methods and analysis of satellite imageries.

Lineaments constitute an interesting approach in the geological mapping and the exploration of resources such as minerals, energy and groundwater. With the use of remote sensing technology structural lineaments can be better detected, due to strong advances in using data and methods that enable us to exceed the usual classical procedures and achieve more precise results. The aim of this work is combining and comparing different techniques of automatic extraction of lineaments in the Palaeozoic massif of Rehamna located in the western Moroccan Meseta. Three methods were used to extract lineaments from Landsat 8 Oli, Sentinel 1 and DEM images. In the first method, four derived images were generated by applying frequential directional filters, in all possible directions (NS, NE-SW, EW, and NW-SE), to RGB composite images and the principal component analysis (PCA) of Landsat 8 OLI. These filters increase the image contrast and allow mapping a large number of lineaments. The second method consists to extract lineaments automatically by using level 1 GRD (Ground Range Distance) of Sentinel 1, according to IW mode with the polarizations (VH) and (VV). Finally, shaded relief images were generated from DEM created by radar interferometry with the resolution of 13m, made by two Single Look Complex (SLC) of sentinel 1 images, and the DEM SRTM with a resolution of 30m. The results were compared with pre-existing geological data and Google Earth images, and were satisfying.

The Rif is among the areas of Morocco most susceptible to landslides, because of the existence of relatively young reliefs marked by a very important dynamics compared to other regions. These landslides are one of the most serious problems on many levels: social, economic and environmental. The increase in the frequency and impact of landslides over the past decade has demonstrated the need for an in-depth study of these phenomena, allowing the identification of areas susceptible to landslides. The main objective of this study is to identify the optimal method for the mapping of the area susceptible to landslides in municipality of Oudka. This area has been marked by the largest landslide in the region, caused by heavy rainfall in 2013. Two Statistical Methods i) Regression Logistics (LR) ii) Artificial Neural Networks (ANN), were used to create a landslide susceptibility map. The realization of this susceptibility map required, first, the mapping of old landslides by the aerial photography, the data of the geological map and by the data obtained using field surveys using GPS. A total of 105 landslides were mapped from these various sources. 50% of this database was used for model building and 50% for validation. Eight independent landslide factors are exploited to detect the most sensitive areas: altitude, slope, aspect, distance of faults, distance streams, distance from roads, lithology and vegetation index (NDVI). The results of the landslide susceptibility analysis were verified using success and prediction rates. The success rate (AUC = 0.918) and the prediction rate (AUC = 0.901) of the LR model is higher than that of the ANN model (success rate (AUC = 0.886) and prediction rate (AUC = 0.877)). These results indicate that the Regression Logistic (LR) model is the best model for determining landslide susceptibility in the study area.