Mobile Mapping Systems (MMS) have become important and regularly used platforms for the collection of physical-environment data in commercial and governmental spheres. For example, a typical MMS may collect location, imagery, video, LiDAR and air quality data from which models of the built-environment can be generated. Numerous approaches to using these data to generate models can be envisaged which can help develop detailed knowledge in the monitoring, maintanence and development of our built-environment. In this context, the efficient storing of this raw spatial data is a significant problem such that bespoke and dynamic access is possible for the generation of modeling requirements. This fundamental requirement of managing these data, where upwards of 40 gigabytes per hour of spatial-information can be collected from an MMS survey, poses significant challanges in data management alone. Existing methodologies mantain bespoke, survey oriented approaches to data management and model generation where the original MMS spatial data is not generally used or available outside these requirements. Thus, there is a need for an MMS data management framework where effective storage and access solutions can hold this information for use and analysis in any modeling context. Towards this end we detail our storage solution and the experiments where the procedures for high volume navigation and LiDAR MMS-data loading are analysed and optimised for minimum upload times and maximum access efficiency. This solution is built upon a PostgreSQL Relational Database Management System (RDBMS) with the PostGIS spatial extension and pg bulkload data loading utility.