Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced a significant surge in both civilian and military applications, owing to their versatility and cost-effectiveness. However, a negligent use or the operation with harmful intent of these aircraft poses a potential risk to public safety and the privacy of individuals. In response to this threat, counter-drone systems have been developed in recent years, including technologies to detect, position, and neutralize these aircraft. This paper presents a review of different existing commercial counter-drone systems, the technologies behind detection, tracking, and classification, as well as soft and hard kill mitigation.

Railway infrastructures necessitate the inspection of various elements to ensure operational safety. This study concentrates on five key components: rail, sleepers and ballast, track geometry, and catenary. The operational principles of the primary defect measurement sensors are elaborated, emphasizing the use of ultrasound, eddy currents, active and passive optical elements, accelerometers, and ground penetrating radar. Each sensor type is evaluated in terms of its advantages and limitations. Examples of mobile inspection platforms are provided, ranging from laboratory trains to draisines and track trolleys. The authors foresee future trends in railway inspection, including the implementation of IoT sensors, autonomous robots, and geospatial intelligence technologies. It is anticipated that the integration of sensors within both infrastructure and rolling stock will enhance maintenance and safety, with an increased utilization of autonomous robotic systems for hazardous and hard-to-reach areas.

The use of uncrewed aerial vehicles (UAVs) for autonomous infrastructure inspections offers significant advantages in terms of safety, efficiency, and cost. However, the effectiveness of such operations is heavily influenced by the availability of Global Navigation Satellite System (GNSS) coverage, particularly in challenging environments such as under bridges or urban canyons. This work evaluates the feasibility of automated UAV pillar inspections through GNSS coverage analysis in two representative case studies: the Freixo Bridge in Porto, Portugal, and the Ribadavia viaduct on the A‐52 motorway in Galicia, northwestern Spain. The proposed methodology uses satellite almanac information to estimate GNSS availability beneath these structures and to identify suitable flight windows for autonomous inspection missions. The analysis accounts not only for satellite visibility, but also for the effects of local orography, terrain characteristics, and bridge geometry, which can significantly affect signal propagation through occlusions, reflections, and multipath. In addition, the study includes a comparison between GPS‐only and multiconstellation configurations in order to assess the benefits of increased satellite availability in partially occluded environments. To improve the realism of the coverage estimation, experimental GPS and multiconstellation measurements collected beneath the Ribadavia viaduct were used to calibrate and refine the prediction algorithm under real signal‐blocking conditions. This approach provides a robust framework for future UAV operations, facilitating adaptive route planning and informed decision‐making to ensure reliable and efficient inspections in GNSS‐constrained environments.