## 1 Introduction  
Mobile mapping is currently an emerging technology, which is used to enhance the attractiveness of mobile phone ecosystems by collecting large data sets covering the biggest cities. Automatic techniques to process the data into 3D models is a topic of increasing importance since accurate and intelligent up-to-date 3D roadside information will be needed in the future, especially for vehicle and pedestrian navigation and location-based services. Most mobile mapping systems are based on images taken from mobile systems, but the use of mobile laser scanning (MLS) together with images is also increasing. The advantage of laser scanning is in its high level of automation in creating the geometry for virtual models using point cloud data. MLS is a multi-sensor system that integrates various navigation, laser scanning, and other data acquisition sensors on a rigid, moving platform (typically a van or a car) for acquiring road-side data. The navigation sensors typically include Global Navigation Satellite System (GNSS) receivers and an Inertial Measurement Unit (IMU), while the data acquisition sensors typically include terrestrial laser scanners and imaging systems. Recent studies on MLS systems and their accuracy can be found in [PERSON] _et al._[1], [PERSON] [2], [PERSON] [3], [PERSON] [4], [PERSON] and [PERSON] [5], [PERSON] [6], [PERSON] _et al._[7], [PERSON] and [PERSON] [8], [PERSON] _et al._[9], [PERSON] [10], [PERSON] _et al._[11; 12; 13], [PERSON] and [PERSON] [14], [PERSON] _et al._[15], [PERSON] and [PERSON] [16], [PERSON] [17], [PERSON] _et al._[18], [PERSON] _et al._[19], [PERSON] and [PERSON] [20], [PERSON] and [PERSON] [21], [PERSON] _et al._[22] and [PERSON] and [PERSON] [23; 24; 25]. Mobile laser scanning systems are being developed both in the field of robotics and surveying. Relatively complete lists of MLS systems have been presented by [PERSON] [17] and [PERSON] [26]. The experiences gained in earlier remote sensing research have shown that permanent test fields with accurate ground truth are valuable tools for analyzing the performance of remote sensing systems and methods. To be able to compare various systems or methods, the test data should be from a common test field; this has also been demonstrated in an earlier EuroSDR project \"Building Extraction\" [27] and in the EuroSDR/ISPRS project \"Tree Extraction\" [28]. The task of comparing mobile laser scanning systems is challenging as the accuracy of the georeferenced point cloud is highly dependent on the GNSS visibility during data acquisition and the satellite geometry is constantly changing. Also, the seasons of the year can affect satellite visibility if there are tall deciduous trees close to the trajectory. Prior to the present study, there have been only a few studies that have focused on MLS comprehensively in combination with test fields. System manufacturers have carried out and published their own tests, but only a few publications exist where system performance has been examined using an established test field and where the results have been analyzed by an independent actor. [PERSON] _et al._[1] used RTK-GPS measurements to collect reference data on two test sites to validate the geometric accuracy of the Streetmapper MLS system. The main focus then was on elevationaccuracy, while only a few control points, measured on white line markings on the road, were used for the analysis of planimetric accuracy. Researchers of the University of California at Davis, United States, used total station and static TLS data to analyze the accuracy of MLS systems (Streetmapper 360, Optech Lynx and Ambercore Titan) when producing digital terrain models of pavement surfaces [29]. Then only elevation accuracy was the subject of concern. [PERSON] _et al_. [7] demonstrated that the StreetMapper system could produce dense 3D measurements with an accuracy level of 30 mm in good GNSS conditions.