## 4 Recent Advancements in Applications
### Adaptability of Different MMSs in Various Applications  
MMSs exhibit significant adaptability across diverse application scenarios, with their effectiveness largely determined by sensor integration, platform choice, and environmental demands. In urban environments, vehicle-mounted MMSs equipped with LiDAR and multi-camera arrays excel at creating detailed 3D models for infrastructure management, though GNSS signal occlusion in dense areas often necessitates SLAM-based corrections. Conversely, natural resource management relies on UAV-based systems with multi-spectral cameras and LiDAR to monitor vegetation health and biomass, while portable backpack \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Category** & **Selected Literature** & **Key Applications** \\ \hline Mapping & Map updating [144; 145] & -Car-mounted MMS \\  & Robotic 3D mapping [146] & -Deep-learning classification \\  & Real estate & Flood risk mapping [147] & -Per-building flood risk modeling \\  & Mining industries & Outdoor and indoor mapping [129] & -Geotechnical and geological study \\  & Traffic infrastructure road property & -Road property survey [148], highway infrastructure & -Spatial accuracy investigation \\  & survey, road inventory [133], & -Traffic sign detection \\  & Road mapping, inventory, and asset management & evaluation of road infrastructure in urban and rural corridor [149], road & -Lighting poles detection \\  & & urban and rural corridor [149], road refurbishment [150], rockfall risk management [151], road boundary extraction [152] & -Enhanced road safety by hazardous objects monitoring \\  & & Large-scale projects monitoring [153] & -MMS with ground penetrating radar (GPR) \\  & & Tunnel inspection [154] & -Site mapping \\  & Under water & Underwater mapping [155] & -Ocean depth mapping \\  & Low-cost & Combining low-cost UAV footage & -Combining terrestrial MMS and UAV \\  & developments & with MMS point cloud data [156] & -3D urban map generation \\  & Gamification & Identification of road assets [157] & -Applications of game engines \\  & SLAM dataset for urban mobile & -Large open datasets \\  & mapping [114], semantic segmentation [158], map updating & -3D point cloud annotation \\  & & using autonomous vehicles [159] & -MMS data capturing by autonomous vehicles \\  & Localization & SLAM on an MMS with multi-camera and tilted LiDAR [160] & -Customized SLAM application by sensor fusion \\  & Custom MMS & Ground penetrating radar MMS [153] & -Additional sensors \\  & Forgotten cultural heritage under forest environments [161], continuous monitoring [132] & -Indoor and outdoor mapping \\  & & continuous monitoring [132] & -Site geometric documentation \\  & Wireless networks & Network coverage estimation [128] & -Ray tracing using 3D maps \\  & & -Wireless propagation models \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._MMS dominate indoor and confined spaces, leveraging visual-inertial SLAM to overcome GPS-denied conditions. Precision agriculture benefits from UAVs with thermal and multi-spectral sensors for real-time crop analysis, whereas disaster response and underwater mapping demand ruggedized LiDAR or sonar systems tailored to harsh, dynamic environments. The modularity of MMS designs--enabling sensor interchangeability--enhances their versatility, though challenges like foliage obstruction, variable lighting, and data synchronization persist. The adaptability of MMSs is further amplified by advancements in AI and standardization efforts. Machine learning algorithms improve object detection and data fusion, enabling systems to dynamically adjust to environmental variability, such as changing crop stages or urban clutter. However, the lack of universal protocols for sensor calibration and data processing remains a barrier to seamless deployment across heterogeneous scenarios.