When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple’s iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint.

Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV algorithm's effectiveness in identifying adult specimens of Popillia using Near-Infrared sensors on Uncrewed Aerial Systems (UAS). The project, conducted in two vineyards in northern Italy, intends to establish a replicable and standardised data acquisition protocol for future monitoring activities. Insects detected by the CV-based method are validated by manual counting performed by entomologists. In a GIS environment, prescription maps are generated in near real-time to identify where the vineyard is most affected and to guide the drone spraying treatment only on the areas in which the threshold is exceeded. The study demonstrates effective semi-automated monitoring, with a clear correlation between CV-based and manual insect measurements, as indicated by the Pearson correlation coefficients ranging from 0.89 to 0.96. Although the CV-based method may overestimate insect numbers, it provides valuable insights for targeted pest management interventions and damage assessment. The project outcomes offer a promising approach to safeguarding agriculture against invasive species, enhancing regional economic resilience while minimising the spread of insecticide, the required time, and human interaction with harmful substances.

The increasing globalisation of trade and climate change are accelerating the spread of invasive pest species, posing significant threats to agriculture. The Japanese beetle (Popillia japonica Newman), first recorded in northern Italy in 2014, is a highly destructive pest with severe economic impacts, particularly in viticulture. Effective monitoring is essential for timely intervention, yet conventional field-based surveys are resource-intensive and limited in spatial coverage.This study presents a novel UAV-based monitoring framework integrating near-infrared (NIR) imaging and machine learning algorithms to detect Popillia japonica adults in vineyard environments. Field experiments were conducted in two commercial vineyards in northern Italy during the beetle's summer flight season. A standardised and replicable aerial data acquisition protocol was developed using lightweight multispectral sensors mounted on rotary-wing UAV platforms. Detected insect signatures were processed through a custom CV pipeline and validated through entomological ground truthing via manual counts.Results show a strong correlation between CV-derived detections and manual observations, with Pearson correlation coefficients ranging from 0.89 to 0.96. Although the system tends to overestimate insect counts under certain canopy conditions slightly, its integration into a GIS environment enabled the near real-time generation of prescription maps. These maps were used to guide site-specific drone spraying treatments, applying insecticides only in hotspot areas where infestation thresholds were exceeded.This UAV-enabled, semi-automated monitoring approach significantly reduces survey time and human exposure to agrochemicals, while supporting precision pest management at scale. The methodology offers a promising framework for integrating remote sensing, AI, and entomological validation, with broader applications for managing invasive species in precision agriculture contexts.

For the machining of industrial components, their accurate alignment on operative machines is a crucial step with significant impacts on the whole process, especially when dealing with large-scale elements. To achieve this alignment, a trained operator is needed along with expensive equipment, such as laser trackers, whose correct use requires the measurements of many significant points and is time-consuming. The lack of a precise alignment comes with possible incorrect parts machining, resulting in quality concerns and potentially expensive rework. To face this problem, collaborative real-time photogrammetry is an effective solution for guiding untrained users in correctly positioning various measured and codified components to generate the best solution for machining high-volume parts. The solution proposed in this contribution is part of the TACCO project, co-financed by EIT Manufacturing (co-funded by the EU). It takes advantage of the TACCO system, where auxiliary components (scale bars, dome-shaped components, coded targets and a cross-reference system) are placed on the object to be measured for their correct positioning in order to obtain an optimal photogrammetric reconstruction. This allows the reduction of the error ellipsoids on several uncoded targets, which are calculated through an iterative simulation carried out in a MATLAB environment. Unskilled operators are then guided through an Augmented Reality headset for their correct placement on the real-world object and for defining the positions and orientations from which to acquire images. This methodology will lead to an accurate measurement of the target points to be machined and, thus, a precise alignment process for the subsequent machining operations.

The sustainable preservation of cultural heritage, encompassing both tangible assets like monuments and intangible expressions such as traditions, necessitates proactive, evidence-based conservation strategies. This paper underscores the importance of rigorous, continuously updated documentation as the cornerstone of planned maintenance practices, an approach that ensures early detection of vulnerabilities, minimizes invasive interventions, and promotes the longevity and integrity of heritage assets. Within this context, UNESCO's World Heritage framework emphasizes the need for detailed documentation and systematic monitoring, particularly for complex sites like Italy’s Sacri Monti, a group of devotional complexes recognized for their integration of landscape, art, and architecture. Focusing on the Sacro Monte di Crea in Piedmont, this study details a case-specific strategy for documenting Chapel XVI, which faces critical conservation issues such as biological growth, salt efflorescence, and material degradation. A multimodal geomatics survey was conducted using terrestrial laser scanning, mobile mapping systems, drone-based photogrammetry, and smartphone LiDAR, enabling the creation of a comprehensive, high-resolution 3D dataset. These datasets were processed, aligned, and fused to produce an accurate and scalable digital representation of the site. This robust foundation supports not only traditional maintenance workflows but also emerging applications of artificial intelligence for 3D reconstruction and interpretation. The integration of such technologies marks a decisive step forward in enhancing both the documentation process and long-term conservation planning for vulnerable cultural heritage sites.

The elective course “UAS for Landscape and Architectural Surveying” offered in the Bachelor programs at the Politecnico di Torino has become increasingly relevant across a wide range of disciplines. These include Architecture, Urban Planning, but also Civil, Building, and Environmental Engineering, as well as Computer, Aerospace, Cinema, and Digital Media Engineering, fields in which Unmanned Aircraft Systems (UAS) are proving valuable beyond traditional surveying applications. Launched in 2017 through a collaboration between two Department of the Politecnico di Torino, the course has witnessed growing student enrollment, reflecting a rising interest in leveraging UAS to simplify, automate, and accelerate various tasks. Rooted in a learning-by-doing pedagogy, the course is organized into four main components. It begins with theoretical lessons covering drone legislation and classification, digital photogrammetry fundamentals, computer vision, and the Structure from Motion (SfM) methodology. This is followed by hands-on training in safe flying environments, where students pilot drones and carry out an actual survey of a designated site. In the next phase, students process datasets, particularly the ones they captured, using commercial SfM software. The final segment includes seminars showcasing drone applications in areas such as early warning, precision agriculture, and humanitarian missions. The course concludes with each group presenting their project, critically analysing results and tailoring discussions to their individual academic backgrounds, highlighting UAS versatility across disciplines.