## 1 Introduction
A beneficial classification of MMSs could be done by considering possible platforms. An MMS could be installed on a variety of stands, each tailored for specific environments, e.g., a backpack or trolley-based MMS is designed to operate in indoor settings or natural environments such as forests, where specific mobility is essential [21; 22; 23]. Vehicle-mounted MMS platforms are optimized for large-scale data collection in urban areas, facilitating efficient mapping of cities. UAV MMSs are designed to be light-weight for large-scale aerial data capturing. Miniaturized platforms, such as smartphones equipped with MMS sensor technologies [24; 25], are developed for general-purpose applications, making spatial data collection accessible to everyday users across various contexts. While MMSs conceptually encompass all sensor-mounted mobile platforms--including UAVs, handheld SLAM devices, and shoulder-borne systems--this review primarily focuses on vehicle-borne MMSs due to their maturity, widespread adoption in geospatial industries, and capacity to integrate multi-sensor suites (e.g., LiDAR, GNSS, IMU, and multi-camera arrays). Where relevant, comparisons are drawn to UAV or portable systems to highlight technological synergies or trade-offs. This scope allows for an in-depth analysis of calibration, sensor fusion, and standardization challenges specific to large-scale, high-accuracy applications in urban and environmental domains. A significant recent development in the MMS industry is related to the advancement of machine learning (ML) methods, particularly as part of artificial intelligence (AI) technologies, which have seen substantial progress since the early 2000s. Deep learning methods have provided MMS developers with powerful ML tools, unlocking new possibilities for real-time data processing and analysis [26]. The integration of AI and ML methods has opened new frontiers in real-time data processing, object detection, and autonomous navigation, further expanding the potential applications of MMS. Applications such as real-time pedestrian detection, autonomous vehicle recognition, high-speed image matching, and road sign detection represent just a few of the many potential uses [27; 28]. MMS now serves as critical data providers for these AI-driven applications, offering high-quality spatial data that enhances the accuracy and functionality of ML models. The integration of AI and ML into MMS technologies is expected to continue driving innovation, transforming industries such as autonomous driving, urban planning, and smart city development. Currently, numerous commercial and research prototypes of MMSs are tailored for specific applications; however, a significant challenge remains: the lack of standardization in MMS design and protocols [29]. This inconsistency hinders the scientific community's ability to fully leverage customizable MMS technologies. The objective of this work is to review and compare existing state-of-the-art MMS technologies, highlight some important recent advancements, and propose a standardized framework for customizable MMS. The discussions in this work will hopefully facilitate the development of more advanced MMS systems, enhancing their functionality and broadening their applications. This paper is structured into four main sections. Section 2 provides a brief overview of recent technological and methodological advancements in sensor development. Section 3 reviews and compares the latest experimental, scientific, and commercial MMS technologies. The most important recent applications of MMS technologies are reviewed and stated in Section 4. Finally, the most important leading factors, such as lack of standardization and the possible future direction of the MMSs, are discussed in Section 5. This comprehensive approach seeks to enhance the usability and effectiveness of MMSs across various research contexts.