Positioning, navigation, and timing (PNT) information is critical for a range of critical civil, industrial, and defense applications. PNT relies predominantly on global navigation satellite systems (GNSSs), but GNSS signals are becoming increasingly vulnerable to natural and man-made disruptions, which motivated the development of alternative PNT technologies that can complement or substitute GNSS in denied environments. These approaches fall into three categories: opportunistic, infrastructure-based, and environment-based PNT. While numerous studies have examined individual techniques, the literature remains fragmented and lacks a comprehensive review. This paper reviews and synthesizes recent advances across all three categories, highlighting application scenarios, achieved accuracy, and limitations. Opportunistic PNT, i. e., using signals of opportunity, has evolved rapidly over the past decade with the introduction of 5G cellular systems and the large-scale deployment of low Earth orbit constellations, although their accuracy remains at the meter level. Infrastructure-based methods can achieve meter- to subcentimeter-level accuracy, but with trade-offs between accuracy, coverage, and reliability. Environment-based PNT requires no external infrastructure and continues to advance through improvements in visual and lidar simultaneous localization and mapping (SLAM) algorithms, while radar-SLAM offers a more robust alternative in environments where visual- or lidar-based systems may struggle. Despite all these alternatives, GNSS remains unmatched due to decades of infrastructure investment and operational evolution. Future resilient PNT in GNSS-denied environments will rely on adaptive multi-sensor integration, combining ambient signals, dedicated infrastructure, and SLAM to meet application-specific needs.

Post-earthquake damage assessment of churches is a critical task, due to the architectural complexity and the safety risks associated with on-site inspections, which expose surveyors to potentially dangerous structurally compromised areas. Among visible indicators, cracks represent one of the most relevant features for evaluating structural damage. As part of the wider project RAISE (NRRP-ECS00000035), this study proposes an image-based deep learning approach for automatic crack detection in church buildings, with the aim of supporting traditional survey procedures and reducing both inspection time and operator risk. A convolutional neural network based on transfer learning is developed and evaluated using the Xception architecture. The model is first trained on a large benchmark dataset already available of concrete crack images. Image patches are extracted and classified in a binary framework (crack vs. non-crack), and model performance is assessed using standard metrics such as Precision, Recall, and F1-score. While initial training on concrete images yields satisfactory results (98% accuracy), performance drops significantly when applied to church images, highlighting a strong domain shift. Retraining with church-specific data, a custom dataset collected from churches in the Liguria region, including surfaces characterized by frescoes, plaster, and decorative elements, substantially improves performance. It achieves a Precision of 93.87%, a Recall of 88.45%, and an F1-score of 91.08% on frescoed surfaces of the vaults, a fundamental element in the damage survey phase. Additional tests investigate the influence of lighting conditions, image resolution, and capture distance, providing practical guidance for acquisition protocol design. The results demonstrate the potential of deep learning techniques for supporting crack detection in heritage contexts, while also highlighting current limitations related to data variability and patch-level classification. Future developments will focus on dataset expansion, pixel-level segmentation approaches, and integration with standardized damage assessment frameworks.

The monitoring of large infrastructures such as bridges, dams, and Tailings Storage Facilities (TSFs) is critical for ensuring structural safety and preventing catastrophic failures. Traditional geodetic monitoring approaches, while accurate, are often labour-intensive, expensive, and impractical for large-scale or remote deployments. This study evaluates the capability of dual-frequency low-cost GNSS receivers (ublox ZED-F9R) integrated with a minicomputer to measure millimeter-scale movements over extended monitoring periods. Two measurement campaigns are conducted: a 16-hour short-term test and a 60-day long-term deployment. A rigid aluminium beam with photogrammetrically measured baseline served as ground truth for assessing positioning accuracy. Short-term experiments demonstrated sub-millimeter accuracy while the 60-day campaign achieved 3D baseline measurement accuracy and precision below 2 mm despite significant environmental variations. The results confirm that low-cost dual-frequency GNSS systems can reliably detect centimeter/year-level deformations, making them suitable for monitoring slow-moving processes in critical infrastructure. The collected data, including raw GNSS observations, processed coordinates, and meteorological data, is publicly available for research purposes at https://doi.org/10.5281/zenodo.17378723.

Photogrammetric 3D surveys generally rely on high-end professional cameras and ground control points, whose placement and measurement can be time-consuming, error-prone, or even hazardous in inaccessible environments. GNSS-assisted photogrammetry provides a viable alternative by directly linking image capture with centimetre-level GNSS positioning, eliminating the need for GCPs. However, such systems typically rely on high-end cameras and professional GNSS equipment, which are costly, bulky, and demand expert operation. Recent advances in smartphone technology, combining high-resolution cameras, multi-frequency GNSS, and inertial sensors, suggest the potential for these devices to function as integrated platforms for low-cost, lightweight, and accessible photogrammetric surveying. Despite challenges related to antenna quality, signal noise, and limited documentation of sensor characteristics, postprocessing strategies have shown that smartphones can achieve decimetre- to centimetre-level positioning accuracy under favourable conditions. This study investigates the performance of an Android smartphone equipped with a dual-frequency (L1, L5) GNSS receiver and high-resolution camera as a standalone device for terrestrial photogrammetry. The research evaluates its capacity to deliver metrically accurate, georeferenced 3D models without external surveying instruments, thereby assessing the feasibility of smartphones as a compact, cost-effective alternative to professional-grade systems in field mapping applications.

Road inspections are essential for assessing pavement conditions and planning maintenance to extend infrastructure lifespan. Cracks are key indicators of distress, and their timely detection can reduce repair costs. While manual inspections are costly and subjective, automated crack detection using vehicle-mounted cameras and digital image processing has improved efficiency but still requires extensive manual review. Crack detection research typically focuses on improving the accuracy of the detection and feature extraction, neglecting the geospatial mapping part which is essential in pavement inspection. This paper presents an end-to-end pipeline that combines V-SLAM, deep learning (DL) and 2D-3D mapping to detect and map pavement cracks using vehicle-mounted cameras footage and provide georeferenced results interpretable inside a Geospatial Information System (GIS). Results significantly reduce inspection time by providing preliminary detections for rapid verification by expert operators in GIS environments. This enhances data management, efficient validation, advanced spatial analysis and time-based tracking of crack progression, ensuring informed decision-making and optimized maintenance planning, ultimately extending infrastructure lifespan and reducing costs.