Being sustainable, clean, and eco-friendly, photovoltaic technology is considered as one of the most hoped solutions face to worldwide energetic challenges. Morocco joins this context with the inauguration of numerous clean energy projects. However, one key factor in making photovoltaic installations a profitable investment are regular and effective inspections in order to detect occurred defects. Unmanned aerial vehicles (UAV) are increasingly used in various inspection fields. In this respect, this work focuses on the use of thermal and visual imagery taken by UAV in the inspection of photovoltaic installations. Visual and thermal images of photovoltaic modules, obtained by UAV, from different installations, and with different acquisition conditions and parameters, were exploited to generate orthomosaics for inspection purposes. The methodology was tested on a dataset we have acquired by a mission in Rabat (Morocco), and also on external datasets acquired in Switzerland. As final results, several visual defects were detected in visual RGB and thermal orthomosaics, such as cracks, soiling, and hotspots. In addition, a procedure of semi-automatic hotspots’ extraction was also developed and is presented within this work. On the other side, various tests were conducted on the influence of some acquisition and processing parameters (images’ overlap, the ground sampling distance, the flying height, the use of ground control points, the internal camera parameters’ optimization) on the detection of defects and the quality of visual and thermal generated orthomosaics. In the end, the potential of UAV thermal and visual imagery in the inspection of photovoltaic installations was discussed in function of various parameters. On the basis of the discussion feedback, UAV were concluded as advantageous tools within the thematic of this project, which proves the necessity of their implementation in this context.

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.

Building information modelling (BIM) is increasingly appropriate for infrastructure projects, and in particular for transport infrastructure. It is a digital solution that integrates the practices of the construction industry in facility management during the whole life cycle. This integration is possible through a single tool, which is the 3D digital model. Nevertheless, BIM standards, such as industry foundation classes, are still in the pipeline for infrastructure management. These standards do not fully meet the requirements of operation and maintenance of transport infrastructure. This paper shows how BIM could be implemented to address issues related to the operation and maintenance phase for transport infrastructure management. For this purpose, a new ontological approach, called Industry Foundation Classes for Operation and Maintenance of Infrastructures (IFCInfra4OM), is detailed. This ontology aims to standardise the use of building information modelling for operation and maintenance in road infrastructures. To highlight the interest of the proposed ontological approach, a building information model of a section on the A7 Agadir–Marrakech Highway in Morocco is produced according to IFCInfra4OM. The methodology is presented. The results obtained, including the IFCInfra4OM data model, are submitted. In the last section, an overview of the IFC extension approach is submitted.

Unmanned aerial vehicles (UAV) are increasingly used in various inspection fields. In this context, the present work focuses on the use of thermal infrared and visual imagery acquired by UAV in the inspection of photovoltaic installations. In the experiment presented within this work, visual and thermal images of photovoltaic modules, obtained by UAV were exploited to generate orthomosaics in the aim of inspecting targeted installations. The methodology was tested on a dataset we have acquired from a mission on Rabat (Morocco). As results, several visual defects were found in visual RGB orthophotos such as cracks and soiling, whereas thermal ones didn't reveal any abnormal profile temperatures. A methodology of hotspots' automatic extraction is also presented and tested on a dataset acquired on Switzerland. In the end, the potential of UAV thermal and visual imagery in the inspection of photovoitaic installations is discussed in different contexts. On the basis of the discussion feedback, UAV were concluded as advantageous tools within the thematic of this project, which proves the necessity of their implementation in the Moroccan context.

UAV-Enabled Corridor Photogrammetry is applied to survey linear transport infrastructure projects’ sites. The corridor flight missions cause a misalignment of the point cloud called the “bowl” effect. The purpose of this study is to offer a methodology based on statistical compensation methods to mitigate this effect and to improve the accuracy and density of the generated point cloud. The aerial images’ post-processing was carried out by varying the aerotriangulation methods. Subsequently, the accuracy improvement was completed by integrating the coordinates of the ground control points (GCPs) through different spatial distributions. Finally, Mean and RANSAC compensations were proposed to address the errors induced by the “bowl” effect on the coordinates of the images’ perspective centres (PCs). The findings indicate that the optimised aerotriangulation using Post-Processed Kinematic (PPK) data significantly contribute to reducing the “bowl” effect. Moreover, the GCP pyramidal spatial distribution allows accuracy improvement to a centimetre level. The Mean compensation method yields optimal outcomes in accuracy. It also helps to optimise on-site survey time and computing resources. RANSAC compensation optimises the accuracy and allows the retrieval of a 5-times-denser point cloud. Furthermore, the results give better accuracy compared to some current approaches.