## 5 Discussion and Future Directions  
Despite the considerable progress in various directions of MMS development, it still seems that more unified developments are required to lower the significant gaps between commercial and research MMSs. This requires open-sourcing MMS designs to gradually optimize hardware and software platforms. While the significant advancements in MMS technologies have been highlighted, several areas remain for further exploration. First, the integration of MMSs with emerging technologies such as 5G, edge computing, and the Internet of Things (IoT) could further enhance real-time data processing and communication capabilities. Additionally, the development of more robust calibration techniques and error correction algorithms will be crucial in improving the accuracy and reliability of MMS data, particularly in challenging environments such as urban canyons or dense forests. Another area for future research is the ethical and societal implications of widespread MMS adoption. As MMS technologies become more pervasive, issues related to data privacy, security, and equitable access must be addressed. Ensuring that MMS technologies are used responsibly and ethically will be essential in maintaining public trust and maximizing their societal benefits. One of the most important ethical concerns is the potential for MMSs to infringe on individual privacy. MMSs can capture detailed 3D models of urban environments, including private properties, vehicles, and even individuals. This raises questions about consent and data ownership. In many cases, individuals are unaware that their properties or activities are being recorded by MMSs. This lack of transparency can lead to privacy violations, especially when data are used for purposes beyond its original intent (e.g., commercial use or law enforcement). While anonymization techniques can be applied to MMS data, the high resolution and spatial accuracy of the data make it difficult to fully anonymize individuals or properties. This is particularly problematic in urban environments where individuals can be identified through contextual information (e.g., vehicle license plates or building details). The use of MMS for surveillance by governments or private entities further exacerbates these concerns. For instance, MMS data could be used to track individuals' movements or monitor public spaces without their knowledge. This raises ethical questions about the balance between public safety and individual privacy. An essential aspect of MMSs relates to real-time processing of collected data to produce timely analytics. Restriction in computational resources is a significant burden in this effort. Therefore, it is worth investing in solutions such as efficient implementations, parallel computing, and the use of computation infrastructures such as local and cloud graphical processing unit (GPU) clusters. Despite the fact that combining homogenous and non-homogenous data has been considerably progressed, especially in methods such as SLAM, noticeable scientific challenges could still be observed, e.g., in merging photogrammetric data with other data sources such as multi-spectral, hyper-spectral cameras and LiDARs in an MMS. More accurate and smarter calibration and data fusion schemes could potentially address such shortcomings. Choosing between photogrammetric and LiDAR point clouds needs to be considered in the MMS design phase. Photogrammetric point clouds, despite being comparable to LiDAR point clouds in terms of accuracy, need a significant post-processing time. Photogrammetric point clouds could also suffer from a lack of density, especially in cases where a lack of sufficient texture information exists. On the other hand, LiDARs are more expensive than cameras and sometimes heavy. The weight aspect of LiDAR becomes important, especially when MMS is placed on platforms such as UAVs. This trade-off is worth investigating carefully based on their principal similarities, differences, costs, and requirements.