Using already existing data is a good way to save both money and time. In this case it’s about Laserdata Nedladdning NH, a nationwide height model created by Lantmäteriet under a 10-year period. But is this data always trustworthy? To perform sample controls on this data is important to know when and how to properly use it. To execute a sample control of this type the technical specification SIS-TS 21144:2016 was used as a guideline throughout the study. This specification is applied when producing a digital surface model at the behest of the contractor to ease the cooperation between this part and the surveyor. In this study there will be research on whether the specification is of relevance as a control document for height models produced with Laserdata Nedladdning NH and how filtering down the data affects its uncertainty.

This control was performed on a parking lot and greenery at University West. The measurements were performed with a total station, established with the help of a GNSS-integrated method, RUFRIS, since there were no known reference points to be used as back objects. Neither were there any fitting places to put any such markers up on. To control the station several markers were put into the surrounding asphalt and grass that were measured right after establishing and after measuring all the control profiles. 18 control profiles were measured, spread around the area in lines of at least 20 m and containing at least 20 points. The point cloud from Laserdata Nedladdning NH was filtered down to different levels of point density which were then tested with different cell sizes.

The results showed that the surface model passed most of the requirements stated by the specification, with an average height difference of just under 1 cm, but showed that areas with sharp height differences would create divergent results. Similarly, the same applied when the point cloud was filtered down according to the specifications most dense least allowed points/ha, where the numbers began to spread out increasingly. In the most thinned out cloud, 50 m cell size, the data quality barelyheld for an older height model, with average height differences up to 2.99 m, which was made with this cell size. From the results a conclusion could be drawn that the technical specification SIS-TS 21144:2016 is relevant for investigations of these models, although not where the ground has too much of an irregular shape. The specification is also not recommended when filtering down measurement data unless it’s performed on a plain open field. 

The Russian invasion of Ukraine does not only affect one country and its citizens, it affects a whole world in both humanitarian and economic aspects likewise the surface of the earth, buildings and the infrastructure of the areas it affects.

To quote Neville Chamberlain: “In war, whichever side may call itself the victor, there are no winners, but all are losers.”

The purpose of the study is to research and analyze earth deformations that have occured due to war focusing on the highly topical conflict in Ukraine. The geographic focus of the analysis is chosen for the city center of the Ukrainian city Mariupol and its immediate vicinity. The method of the study is to implement a remote analysis using the Copernicus program formed by the European Space Agency, ESA. The Copernicus program consists of several different satellite missions where the main purpose of the Sentinel-1-mission includes, among other assignments, surveillance of earth defromations by data collection by Interferometric Synthetic Aperture Radar, InSAR. By making an interferogram, by use of satellite pictures from the Sentinel-1 before and after the outbreak of the Russian invasion, the SNAP-application from the toolbox of the ESA and Differential InSAR it is possible to analyze the earth deformations due to the war in Ukraine.

Satellite images from different dates have been downloaded to cover a wide range of time intended to secure data for analysis.

Raw data have been downloaded and processed by SNAP-applications and its different available tools which have resulted in an interferogram which has been the basis for the analyses. The results obtained were possibly caused by human activity on ground and buildings in Mariupol and its immediate vicinity however the result could not be unambiguously ensured and there is a need of further investigation as well as an in-depth knowledge in as well InSAR as the SNAP-application.

Recent technical development of measuring instruments and the pursuit of digitized community building are creating demand for digital property boundaries with requirement for low location uncertainty. Progressively property boundaries need to be carefully measured in order to be managed and placed correctly in digital map solutions that are not of high quality today. The property's boundary markings are sometimes located in hard-to reach places and cannot always be measured using standard measurement methods, solutions are needed to be able to determine points that are not directly accessible. When points are obscured for direct measurement, they are usually called hidden points.

To determine the position of such hidden points, e.g., Network RTK (NRTK) is used for indirect measurement with a collinear polar method. This means that with the help of two measured points on a line, a third point (hidden point) on the same line can be determined individually if the distance from one of the measured points to the hidden point has also been measured. We are particularly interested in the measurement reliability of the hidden points and will, with the help of experiments in different configurations, and will tune it. The 4 different measurement series that this report includes are made with the methods for extrapolation and interpolation, where there are two measurements of each method. Each measurement series contains 10 measurements. Point I is in this case the hidden point, in measurement series 1–3 uses calculation of the uncertainty based on point B, which in the measurements is always closest to point I. Series 4 is calculated with both point A and point B, which means that it is important to measure both distances to the hidden point I. Even though the first two measurements are extrapolation, there is a big difference between the results, the reason for this is that the distances between points I and B are longer than points B and A in measurement series 1.

The possibility to measure in so-called hidden points provide sufficiently low measurement uncertainty when it comes to measuring property boundaries in, for example, forest environments. The measurement uncertainty for property boundaries in the country is 150 mm and with the project's measurements and calculations, the greatest measurement uncertainty is 69 mm, which is the most probable value. To continue to investigate whether the formulas are reliable, significantly more measurements need to be performed, it is also possible to measure in both planes and heights and with a total station.

The quality of angle measurements is important, otherwise can lead to uncertain results and costly delays. In some cases, this quality is unknown due to the age or wear of the instrument, therefore it is important to be able to estimate the quality of anglemeasurements. The purpose of this study is to estimate the horizontal angle quality of a Leica VIVA TS15 G 3” r1000 total station tunnelling station. This instrument should measure horizontal angles with an accuracy of 1mgon.

To accomplish this the directional method was used, which utilizes the method of least squares, and which only requires directions for estimation. The total station was levelled indoors, and it was examined for instrumental errors. Afterwards the angles in a circle were measured clockwise in the direct mode, then the angles in a counter-clockwise, this constituted one full repetition. In this study, 20 repetitions were conducted for 4, 6, 8 and 10 angles.

Next, the variance of unit weight and the residuals were estimated with the least squares method. The residuals were analysed with quantile-quantile plots, histograms, as well as kurtosis and skewness tests to determine if they followed a normal distribution. Another statistical test was carried out with a 95% level of confidence, to determine if the estimated variance and the nominal variance of the total station were equivalent. Only the observations with 6 horizontal angles were deemed equivalent with the nominal variance. Due to this conflicting answer another statistical test was preformed to assess if the estimated variances with 4, 8 or 10 angles were equivalent with the variance of 6 horizontal angles. They were not equivalent with a 95% level of confidence. It was deemed the total station could measure with the nominal accuracy of 1mgon with a small number of repetitions.

Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.