Global awareness of environmental issues has boosted interest in renewable energy resources, among which solar energy is one of the most attractive renewable sources. The massive growth of PV plants, both in number and size, has motivated the development of new approaches for their inspection and monitoring. In this paper, a rigorous drone photogrammetry approach using optical Red, Green and Blue (RGB) and Infrared Thermography (IRT) images is applied to detect one of the most common faults (hot spots) in photovoltaic (PV) plants. The latest advances in photogrammetry and computer vision (i.e., Structure from Motion (SfM) and multiview stereo (MVS)), together with advanced and robust analysis of IRT images, are the main elements of the proposed methodology. We developed an in-house software application, SunMap, that allows automatic, accurate, and reliable detection of hot spots on PV panels. Along with the identification and geolocation of malfunctioning PV panels, SunMap provides high-quality cartographic products by means of 3D models and true orthophotos that provide additional support for maintenance operations. Validation of SunMap was performed in two different PV plants located in Spain, generating positive results in the detection and geolocation of anomalies with an error incidence lower than 15% as validated by the manufacturer’s standard electrical tests.

The three-dimensional registration of industrial facilities has a great importance for maintenance, inspection, and safety tasks and it is a starting point for new improvements and expansions in the industrial facilities context. In this paper, a comparison between the results obtained using a novel portable mobile mapping system (PMMS) and a static terrestrial laser scanner (TLS), widely used for 3D reconstruction in civil and industrial scenarios, is carried out. This comparison is performed in the context of industrial inspection tasks, specifically in the thermal and fluid-mechanics facilities in a hospital. The comparison addresses the general reconstruction of a machine room, focusing on the quantitative and qualitative analysis of different elements (e.g., valves, regulation systems, burner systems and tanks, etc.). The validation of the PMMS is provided considering the TLS as ground truth and applying a robust statistical analysis. Results come to confirm the suitability of the PMMS to perform inspection tasks in industrial facilities.

The accurate and reliable extraction and matching of distinctive features (keypoints) in multi-view and multi-modal datasets is still an open research topic in the photogrammetric and computer vision communities. However, one of the main milestones is selecting which method is a suitable choice for specific applications. This encourages us to develop an educational tool that encloses different hand-crafted and learning-based feature-extraction methods. This article presents PhotoMatch, a didactical, open-source tool for multi-view and multi-modal feature-based image matching. The software includes a wide range of state-of-the-art methodologies for preprocessing, feature extraction and matching, including deep learning detectors and descriptors. It also provides tools for a detailed assessment and comparison of the different approaches, allowing the user to select the best combination of methods for each specific multi-view and multi-modal dataset. The first version of the tool was awarded by the ISPRS (ISPRS Scientific Initiatives, 2019). A set of thirteen case studies, including six multi-view and six multi-modal image datasets, is processed by following different methodologies, and the results provided by the software are analysed to show the capabilities of the tool. The PhotoMatch Installer and the source code are freely available.

This article presents a comprehensive forensic tool for crime scene and traffic accident investigations, integrating advanced 3D reconstruction and semantic and dynamic analyses; the tool facilitates the accurate documentation and preservation of crime scenes through photogrammetric techniques, producing detailed 3D models based on images or video captured under specified protocols. The system includes modules for semantic analysis, enabling object detection and classification in 3D point clouds and 2D images. By employing machine learning methods such as the Random Forest model for point cloud classification and the YOLOv8 architecture for object detection, the tool enhances the accuracy and reliability of forensic analysis. Furthermore, a dynamic analysis module supports ballistic trajectory calculations for crime scene investigations and the vehicle impact speed estimation using the Equivalent Barrier Speed (EBS) model for traffic accidents. These capabilities are integrated into a single, user-friendly platform offering significant improvements over existing forensic tools, which often focus on singular tasks and require expertise. This tool provides a robust, accessible solution for law enforcement agencies, enabling more efficient and precise forensic investigations across different scenarios.

Photovoltaic solar plants are one of the main facilities away from urban centers for the generation of clean energy. Since its appropriate maintenance ensures its suitable operation, optimizing their maintenance tasks in a preventive way is key. This article presents a spatial data infrastructure called INSPECTORMAP that, based on the analysis of free satellite images within the optical spectrum, can detect unusual vegetation and bodies of water in the vicinity of photovoltaic plants that can affect their correct operation. Thanks to the implementation of a monitoring and alert system, it is possible to know and map the status of the photovoltaic plant in terms of unusual coverages appearing, both natural and artificial, at any moment. Thus, maintenance workers would travel to the solar plant to carry out their maintenance tasks in this regard only when the system detects a risk.

This paper presents a new tool for external quality control in welds using close-range photogrammetry. The main contribution of the developed approach is the automatic assessment of welds based on 3D photogrammetric models, enabling objective and accurate analyses through an in-house tool that was developed, WELDMAP. As a result, inspectors can perform external quality control of welds in a simple and efficient way without requiring visual inspections or external tools, and thus avoiding the subjectivity and imprecisions of the classical protocol. The tool was validated with a large dataset in laboratory tests as well as in real scenarios.