Agriculture consumes an important ratio of the water reserve in irrigated areas. The improvement of irrigation is becoming essential to reduce this high water consumption by adapting supplies to the crop needs and avoiding losses. This global issue has prompted many scientists to reflect on sustainable solutions using innovative technologies, namely Unmanned Aerial Vehicles (UAV), Machine Learning (ML), and the Internet of Things (IoT). This article aims to present an overview of the use of these new technologies in the analysis of the water status of crops for better irrigation management, with an emphasis on arboriculture. The review demonstrated the importance of UAV-ML-IoT technologies. This contribution is due to the relevant information that can be collected from IoT sensors and extracted from UAV images through various sensors (RGB, multispectral, hyperspectral, thermal), and the ability of ML models to monitor and predict water status. The review in this paper is organized into four main sections: the use of UAV in arboriculture, UAV for irrigation management in arboriculture, IoT systems and irrigation management, and ML for data processing and decision-making. A discussion is presented regarding the prospects for smart irrigation using geospatial technologies and machine learning.

This work outlines a new approach for the integration of 3D Building Information Modelling and the 3D Geographic Information System (GIS) to provide semantically rich models, and to get the benefits from both systems to help document and analyse cultural heritage sites. Our proposed framework is based on the Jeddah Historical Building Information Modelling process (JHBIM). This JHBIM consists of a Hijazi Architectural Objects Library (HAOL) that supports higher level of details (LoD) while decreasing the time of modelling. The Hijazi Architectural Objects Library has been modelled based on the Islamic historical manuscripts and Hijazi architectural pattern books. Moreover, the HAOL is implemented using BIM software called Autodesk Revit. However, it is known that this BIM environment still has some limitations with the non-standard architectural objects. Hence, we propose to integrate the developed 3D JHBIM with 3D GIS for more advanced analysis. To do so, the JHBIM database is exported and semantically enriched with non-architectural information that is necessary for restoration and preservation of historical monuments. After that, this database is integrated with the 3D Model in the 3D GIS solution. At the end of this paper, we’ll illustrate our proposed framework by applying it to a Historical Building called Nasif Historical House in Jeddah. First of all, this building is scanned by the use of a Terrestrial Laser Scanner (TLS) and Close Range Photogrammetry. Then, the 3D JHBIM based on the HOAL is designed on Revit Platform. Finally, this model is integrated to a 3D GIS solution through Autodesk InfraWorks. The shown analysis presented in this research highlights the importance of such integration especially for operational decisions and sharing the historical knowledge about Jeddah Historical City. Furthermore, one of the historical buildings in Old Jeddah, Nasif Historical House, was chosen as a test case for the project.

Open Building Information Modelling (OpenBIM) is a collaborative project management process. Its application to road infrastructures is currently limited. OpenBIM standards for infrastructure are still under development. One of these standards is the Industry Foundation Classes (IFC), which is a data architecture for modelling infrastructure projects. The current and upcoming releases of IFCRoad focus on structuring data for the design and construction phases of an infrastructure’s lifecycle. Semantics of the O&M process phase are not fully integrated within these standards. This paper proposes an extension of the IFC schema to enrich this standard with semantics inherent in the O&M phase of road infrastructures. This extension, based on IFCInfra4OM ontology, allows the OpenBIM process to be fully applied to road infrastructures. Its implementation on a case study relative to the A7 Agadir–Marrakech Highway in Morocco enables, on the one hand, analysis and compliance with O&M management requirements on the basis of a single container: the IFC-BIM-based model. On the other hand, it allows comparison of the OpenBIM process with that of ClosedBIM for the integration of O&M data into BIM for a road infrastructure.

As a consequence of increasing safety concerns, camera surveillance has been widely adopted as a way to monitor public spaces. One of the major challenges of camera surveillance is to design an optimal method for camera network placement in order to ensure the greater possible coverage. In addition, this method must consider the landscape of the monitored environment to take into account the existing objects that may influence the deployment of such a network. In this paper, a new Voronoi-based 3D GIS oriented approach named “HybVOR” is proposed for surveillance camera network placement. The “HybVOR” approach aims to achieve a coverage near 100% through three main phases. First, a Voronoi Diagram from buildings’ footprints is generated and cameras are placed on the Voronoi Edges. Second, the level of coverage is assessed by calculating a viewshed based on a raster Digital Surface Model of the region of interest. Finally, the visibility of the main buildings’ entrances is evaluated based on a 3D vector model that contains these features. The effectiveness of the “HybVOR” approach is demonstrated through a case study that corresponds to an area of interest in Jeddah Seaport in the Kingdom of Saudi Arabia.

Irrigation is crucial for crop cultivation and productivity. However, traditional methods often waste water and energy due to neglecting soil and crop variations, leading to inefficient water distribution and potential crop water stress. The crop water stress index (CWSI) has become a widely accepted index for assessing plant water status. However, it is necessary to forecast the plant water stress to estimate the quantity of water to irrigate. Deep learning (DL) models for water stress forecasting have gained prominence in irrigation management to address these needs. In this paper, we present a comparative study between two deep learning models, ConvLSTM and CNN-LSTM, for water stress forecasting using remote sensing data. While these DL architectures have been previously proposed and studied in various applications, our novelty lies in studying their effectiveness in the field of water stress forecasting using time series of remote sensing images. The proposed methodology involves meticulous preparation of time series data, where we calculate the crop water stress index (CWSI) using Landsat 8 satellite imagery through Google Earth Engine. Subsequently, we implemented and fine-tuned the hyperparameters of the ConvLSTM and CNN-LSTM models. The same processes of model compilation, optimization of hyperparameters, and model training were applied for the two architectures. A citrus farm in Morocco was chosen as a case study. The analysis of the results reveals that the CNN-LSTM model excels over the ConvLSTM model for long sequences (nine images) with an RMSE of 0.119 and 0.123, respectively, while ConvLSTM provides better results for short sequences (three images) than CNN-LSTM with an RMSE of 0.153 and 0.187, respectively.