Tunnels are essential for infrastructure and require regular visual inspections by trained operators to identify defects like cracks and water ingress. This process is time-consuming, prone to human error, and requires tunnel closures. The need for efficient inspection solutions has increased as tunnel networks expand and age. Recent advancements in mobile mapping systems equipped with geomatic sensors, such as cameras and LiDAR sensors, have significantly enhanced data collection. These systems allow for rapid data acquisition, reducing tunnel downtime and enabling the generation of digital twins for remote inspections, which improves knowledge transfer. However, damage detection remains manual even if deep learning methods have been extensively investigated to automate defect detection using high-resolution images collected with mobile mapping systems. This paper is part of the TACK-II project and focuses on mobile mapping systems, providing a detailed analysis of the parameters that influence their design for effective data collection in tunnels.