Mobile mapping systems (MMS) are becoming used in standard geodetic tasks more common in the last years. This paper deals with the accuracy evaluation of two types of data acquired by MMS RIEGL VMX-450, and their comparison. The first type is data from mobile laser scanning (MLS). The second type is mobile photogrammetry data. The new high accurate test point field was built in area of Advanced Materials, Structures and Technologies (AdMaS) research centre that is part of Brno University of Technology. Geodetic network and test point field were measured by Trimble R8s GNSS system and Trimble S8 HP total station. The estimate of the 3D standard deviation determined by an adjustment is 2 mm. The accuracy of MLS and mobile photogrammetry data was tested based on the differences between the coordinates of the points determined from the MMS data and determined by before mentioned high precise measurement. The resulting coordinates from photogrammetric data were determined by manual detection of targets in the images. The estimate of the 3D standard deviation is 0.017 m from the MLS data, and 0.061 m from the mobile photogrammetry data. As we supposed, the mobile laser scanning data are significantly more accurate than mobile photogrammetry data. Achieved accuracy of MLS exceeds the original expectations with respect to the GNSS/IMU positioning accuracy, which is according to the manufacturer RIEGL between 0.02-0.05 m. The same scene is often scanned with multiple scanning passes to ensure high quality of the scanned point cloud, therefore we tested the relative accuracy of mobile laser scanning data from two MMS vehicle passes in the same locality of interest. Two different data sets were evaluated, first data set contains points on roads, second data set on buildings. The standard deviation estimate does not exceed 0.008 m and the maximum absolute deviation does not exceed 0.030 m for both data sets. The difference between the two passes is not significant in comparison with the accuracy criteria required for standard mapping purposes. We also compared automatic point cloud production from photogrammetry data processed in Bentley ContextCapture to the point cloud from laser scanning. The MLS data has been used as a reference because it is significantly more accurate as mentioned before. This comparison was done only on the second data set (buildings). The standard deviation estimate is 0.16 m and the maximum absolute deviation is 0.25 m. Our evaluation contains also statistical testing of outliers and stragglers. In contrast to many authors, we don’t use the simplified approach 3σ rule, and in 1D. We use more exact approach using critical values of the statistics for significance levels α = 5 % and α = 1 % to stragglers and outliers test in 3D.

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Mobile mapping systems (MMS) are becoming widely used in standard geodetic tasks more commonly in the last years. The paper is focused on the influence of control points (CPs) number and configuration on mobile laser scanning accuracy. The mobile laser scanning (MLS) data was acquired by MMS RIEGL VMX-450. The resulting point cloud was compared with two different reference data sets. The first reference data set consisted of a high-accuracy test point field (TPF) measured by a Trimble R8s GNSS system and a Trimble S8 HP total station. The second reference data set was a point cloud from terrestrial laser scanning (TLS) using two Faro Focus3D X 130 laser scanners. The coordinates of both reference data sets were determined with significantly higher accuracy than the coordinates of the tested MLS point cloud. The accuracy testing is based on coordinate differences between the reference data set and the tested MLS point cloud. There is a minimum number of 6–7 CPs in our scanned area (based on MLS trajectory length) to achieve the declared relative accuracy of trajectory positioning according to the RIEGL datasheet. We tested two types of ground control point (GCP) configurations for 7 GCPs, using TPF reference data. The first type is a trajectory-based CPs configuration, and the second is a geometry-based CPs configuration. The accuracy differences of the MLS point clouds with trajectory-based CPs configuration and geometry-based CPs configuration are not statistically significant. From a practical perspective, a geometry-based CPs configuration is more advantageous in the nonlinear type of urban area such as our one. The following analyzes are performed on geometry-based CPs configuration variants. We tested the influence of changing the location of two CPs from ground to roof. The effect of the vertical configuration of the CPs on the accuracy of the tested MLS point cloud has not been demonstrated. The effect of the number of control points on the accuracy of the MLS point cloud was also tested. In the overall statistics using TPF, the accuracy increases significantly with increasing the number of GCPs up to 6. This number corresponds to a requirement of the manufacturer. Although further increasing the number of CPs does not significantly increase the global accuracy, local accuracy improves with increasing the number of CPs up to 10 (average spacing 50 m) according to the comparison with the TLS reference point cloud. The accuracy test of the MLS point cloud was divided into the horizontal accuracy test on the façade data subset and the vertical accuracy test on the road data subset using the TLS reference point cloud. The results of this paper can help improve the efficiency and accuracy of the mobile mapping process in geodetic praxis.

An accurate estimation of an earthquake magnitude plays an important role in targeting emergency services towards affected areas. Along with the traditional methods using seismometers, site displacements caused by an earthquake can be monitored by the Global Navigation Satellite Systems (GNSS). GNSS can be used either in real-time for early warning systems or in offline mode for precise monitoring of ground motion. The Precise Point Positioning (PPP) offers an optimal method for such purposes, because data from only one receiver are considered and thus not affected by other potentially not stable stations. Precise external products and empirical models have to be applied, and the initial convergence can be reduced or eliminated by the backward smoothing strategy or integer ambiguity resolution. The product for the magnitude estimation is a peak ground displacement (PGD). PGDs observed at many GNSS stations can be utilized for a robust estimate of an earthquake magnitude. We tested the accuracy of estimated magnitude scaling when using displacement waveforms collected from six selected earthquakes between the years 2016 and 2020 with magnitudes in a range of 7.5–8.2 Moment magnitude MW. We processed GNSS 1Hz and 5Hz data from 182 stations by the PPP method implemented in the G-Nut/Geb software. The precise satellites orbits and clocks corrections were provided by the Center for Orbit Determination in Europe (CODE). PGDs derived on individual GNSS sites formed the basis for ground motion parameters estimation. We processed the GNSS observations by the combination of the Kalman filter (FLT) and the backward smoother (SMT), which significantly enhanced the kinematic solution. The estimated magnitudes of all the included earthquakes were compared to the reference values released by the U. S. Geological Survey (USGS). The moment magnitude based on SMT was improved by 20% compared to the FLT-only solution. An average difference from the comparison was 0.07 MW and 0.09 MW for SMT and FLT solutions, respectively. The corresponding standard deviations were 0.18 MW and 0.22 MW for SMT and FLT solutions, which shows a good consistency of our and the reference estimates.