The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurate estimation of roof geometry: area, orientation and slope. This paper presents a comparative study between LiDAR data of different point densities: 0.5, 1, 2 and 14 points/m² for the measurement of the area of roofs of residential and industrial buildings. The data used for the study are the LiDAR data freely available by the Spanish Institute of Geography (IGN), which is offered according to the INSPIRE Directive. The results obtained show different behaviors for roofs with an area below and over 200 m². While the use of low-density point clouds (0.5 point/m²) presents significant errors in the estimation of the area, the use of point clouds with higher density (1 or 2 points/m²) implies a great improvement in the area results, with no significant difference among them. The use of high-density point clouds (14 points/m²) also implies an improvement of the results, although the accuracy does not increase in the same ratio as the increase in density regarding 1 or 2 points/m². Thus, the conclusion reached is that the geometrical characterization of roofs requires data acquisition with point density of 1 or 2 points/m², and that higher point densities do not improve the results with the same intensity as they increase computation time.

This study presents a comprehensive exploration centred on the morphology and surface structure of bladeless wind turbines (BWTs) aimed at optimizing their wind energy harvesting capability. Unlike conventional wind technology where vortex-induced vibration (VIV) is seen as problematic due to aeroelastic resonance, this effect becomes advantageous in BWT energy harvesters, devoid of frictional contact or gears. The primary objective of this study is to develop an optimal BWT design for maximizing energy output. Specifically, this study delves into optimizing the energy performance of these VIV wind energy harvesters, investigating how the geometry (shape and roughness) influences their operating range, known as Lock-In range. The results demonstrate how variations in geometry (convergent, straight, or divergent) can shift the Lock-In range to different Reynolds numbers (Re), modelled by the equation: Re (max Lock-In) = 0.30 α + 4.06. Furthermore, this study highlights the minimal impact of roughness within the considered test conditions.

The presence of shadows is one of the main disadvantages of photovoltaic solar panel installations in urban environments. This article analyses their effect on solar installations performed on urban elements where the use of solar energy can be considered novel: bus shelters. For this purpose, the PVGIS tool is used in combination with a new methodology for the extraction of the shadow horizon profile from LiDAR point clouds. The results show a 29.90% and 37% reduction of calculated solar radiation using horizon profiles derived from LiDAR point clouds of 0.5 pts/m² and 1 pt/m², respectively, versus no horizon profile. By taking shading into account, thanks to this study it is possible to make a more realistic prediction of the use of the electrical energy generated by the sun to cover urban energy consumption in bus shelters. Results show that the energy surplus produced overall allows these elements to be converted into charging points for light electric vehicles, allowing up to 35 units per day/bus shelter to be charged. The use of bus shelters as a place to generate clean energy through solar energy and charge light vehicles makes it possible to move towards sustainable and environmentally friendly cities.

This study presents a critical evaluation of image-based 3D reconstruction techniques for small archaeological artifacts, focusing on a quantitative comparison between Neural Radiance Fields (NeRF), its recent Gaussian Splatting (GS) variant, and traditional Structure-from-Motion (SfM) photogrammetry. The research targets artifacts smaller than 5 cm, characterized by complex geometries and reflective surfaces that pose challenges for conventional recording methods. To address the limitations of traditional methods without resorting to the high costs associated with laser scanning, this study explores NeRF and GS as cost-effective and efficient alternatives. A comprehensive experimental framework was established, incorporating ground-truth data obtained using a metrological articulated arm and a rigorous quantitative evaluation based on root mean square (RMS) error, Chamfer distance, and point cloud density. The results indicate that while NeRF outperforms GS in terms of geometric fidelity, both techniques still exhibit lower accuracy compared to SfM, particularly in preserving fine geometric details. Nonetheless, NeRF demonstrates strong potential for rapid, high-quality 3D documentation suitable for visualization and dissemination purposes in cultural heritage. These findings highlight both the current capabilities and limitations of neural rendering techniques for archaeological documentation and suggest promising future research directions combining AI-based models with traditional photogrammetric pipelines.

The rehabilitation of mining environments poses significant challenges due to the contamination risks associated with hazardous materials, such as arsenic and other chemical products. This research study presents the development of a Digital Twin for the Lavrion Technological and Cultural Park (LTCP), a former mining and metalworking site that is currently undergoing environmental restoration. The Digital Twin integrates advanced technologies, including real-time sensor monitoring, geophysical methods, and 3D modeling, to provide a comprehensive tool for assessing and managing the environmental conditions of the site. Key elements of the project include the monitoring of hazardous-waste storage, the evaluation of contaminated soils, and the assessment of the Park’s infrastructure, which includes both deteriorating buildings and successfully restored structures. Real-time sensor data are collected to track critical parameters such as conductivity, temperature, salinity, and levels of pollutants, enabling proactive environmental management and mitigation of potential risks. The integration of these technologies enables continuous monitoring, historical data analysis, and improved decision making in the ongoing efforts to preserve the site’s ecological integrity. This study demonstrates the potential of using Digital Twins as an innovative solution for the sustainable management and valorization of mining heritage sites, offering insights into both technological applications and environmental conservation practices.

Smartvessel is an innovative fire extinguisher prototype supported by new materials and IoT technology that seeks to improve the functionality and efficiency of conventional fire extinguishers. Storage containers for gases and liquids are essential for industrial activity as they enable higher energy density. The main contributions of this new prototype are (i) innovation in the use of new materials that provide lighter and more resistant extinguishers, both mechanically and against corrosion in aggressive environments. For this purpose, these characteristics are directly compared in vessels made of steel, aramid fiber and carbon fiber with the filament winding technique. (ii) The integration of sensors that allow its monitoring and provide the possibility of predictive maintenance. The prototype is tested and validated on a ship, where accessibility is complicated and critical. For this purpose, different data transmission parameters are defined, verifying that no data are lost. Finally, a noise study of these measurements is carried out to verify the quality of each data. Acceptable coverage values are achieved with very low read noise, on average less than 1%, and a weight reduction of 30% is obtained.