In this paper, frontal variations and surface area changes for each of the years 2017-2023 are assessed for 277 Swedish glaciers, of which the majority is contained within the Randolph Glacier Inventory 7.0. Mapping of all Swedish glaciers became possible by combining Sentinel-2 imagery, semi-automated mapping procedures and the open-source Margin Change Quantification Tool (MaQiT). In addition, manual mapping was performed at a subset of 22 glaciers historically associated with the Swedish Front Variation Program. At four of those, mapping accuracy was assessed by contrasting Sentinel-2 mapped fronts to fronts mapped in situ using Global Navigation Satellite System (GNSS), a total station and an uncrewed aerial vehicle. Results show widespread retreat of all Swedish glaciers, with cumulative frontal variation amounting on average to -55.6 m during 2017-2023 or -9.3 m a-1. Swedish glaciers had a total area of similar to 237 km2 in 2017 and of 210 km2 in 2023. The reduction by similar to 27 km2 corresponds to a loss of 11% with respect to the areal extent in the year 2017 but varies across regions. It is also almost as large as the combined area loss of Swedish glaciers in the preceding 15 years (similar to 31 km2, 2002-2017).

The expected accuracies obtainable with Network Real-Time Kinematic (NRTK) measurements are of interest to surveyors working on construction projects. This study introduces an NRTK-based free stationing method called RUFRIS (Real Time Updated Free Station) which is independent from physically marked points. Integration of total station distance and direction observations with NRTK measurements enables uncertainty estimation of both total station and surveyed NRTK points. In Sweden, NRTK is conducted using the national network of permanent reference stations with different densifications (10, 35, 70 km). This paper investigates the applicability of the RUFRIS-method to estimate the uncertainty of NRTK measurements in the field.

Using the Global Navigation Satellite System (GNSS) surveying methods give the users anadvantage over the other traditional methods, where for instance, measurements betweenpoints with longer distances can be performed with no line of sight. Furthermore, the GNSStechnology, through its real-time applications, can provide a basis for more efficient datacollection and product automation, e.g., machine guidance. Nowadays, users do not need tohave deep theoretical knowledge of GNSS to survey. They only expect a reliable and preciseestimation of coordinates from the surveying instruments and GNSS services. It is the systemproviders, who face challenges to ensure a reliable delivery with consistent quality to theusers and are, therefore, in need of planning tools and models to operate more efficientservices. The developed simulation model in this paper provides an overview of uncertaintiesfor the network Real Time Kinematic (RTK) measurements and investigates the effect ofnetwork geometry on users’ precision. Besides, the model is used to study the possibledeterioration in users’ precision if any reference station in the network stops working. Thisinformation provides a theoretical basis for densification strategies and evaluates if anyreference station is more important than the others to maintain consistent quality in thenetwork.

In classical two-dimensional (2D) geodetic networks, reducing slope distances to horizontal ones is an important task for engineers. These horizontal distances along with horizontal directions are used in 2D geodetic adjustment. The common practice for this reduction is the use of vertical angles to reduce distances using trigonometric rules. However, one faces systematic effects when using vertical angles. These effects are mainly due to refraction, deflection of the vertical (DOV), and the geometric effect of the reference surface (sphere or ellipsoid). To mitigate refraction and DOV effects, one can choose to observe the vertical angles reciprocally if the baseline points' elevation difference is small. This paper quantifies these effects and proposes a proper solution to eliminate the effects in small-scale geodetic networks (where the longest distances are less than 5 km). The goal is to calculate slope distances into horizontal ones appropriately. For this purpose, we used the SWEN17_RH2000 quasigeoid model (in Sweden) to study the impact of the DOV applying different baseline lengths, azimuths, and vertical angles. Finally, we propose an approach to study the impact of the geometric effect on vertical angles. We illustrate that the DOV and the geometric effects on vertical angles measured reciprocally are significant if the height difference of the start point and endpoint in the baseline is large. Geometric correction should be considered for the measured vertical angles to calculate horizontal distances correctly if the network points are not on the same elevation, even if the vertical angles are measured reciprocally.

Determination of the Earth’s gravity field and geopotential value is one of the fundamental topics in physical geodesy. Traditional terrestrial gravity and precise leveling measurements can be used to determine the geopotential values at a local or regional scale. However, recent developments in optical atomic clocks have not only rapidly improved fundamental science but also contributed to applied research. The latest generation of optical clocks is approaching the accuracy level of 10−18 when facilitating atomic clock networks. These systems allow examining fundamental theories and many research applications, such as atomic clocks applications in relativistic geodesy, to precisely determine the Earth’s gravity field parameters (e.g., geopotential values). According to the theory of relativistic geodesy, the frequency difference measured by an optical clock network is related to the gravity potential anomaly, provided that the effects of disturbing signals (i.e., tidal and non-tidal contributions) are filtered out. The relativistic geodesy principle could be used for a practical realization of global geodetic infrastructure, most importantly, a vertical datum unification or realization of height systems. This paper aims to review the background of relativistic (clock-based) geodesy and study the variations of optical atomic clock measurements (e.g., due to hydrology loading and land motion).

Multidecadal sea level variation in the Baltic Sea is investigated from 1900 to 2020 deploying satellite and in situ datasets. As a part of this investigation, nearly 30 years of satellite altimetry data are used to compare with tide gauge data in terms of linear trend. This, in turn, leads to validation of the regional uplift model developed for the Fennoscandia. The role of North Atlantic Oscillation (NAO) in multidecadal variations of the Baltic Sea is also analyzed. Although NAO impacts the Baltic Sea level on seasonal to decadal time scales according to previous studies, it is not a pronounced factor in the multidecadal variations. The acceleration in the sea level rise of the basin is reported as statistically insignificant in recent studies or even decelerating in an investigation of the early 1990s. It is shown that the reason for these results relates to the global warming hiatus in the 1950s-1970s, which can be seen in all eight tide gauges used for this study. To account for the slowdown period, the acceleration in the basin is investigated by fitting linear trends to time spans of six to seven decades, which include the hiatus. These results imply that the sea level rise is accelerated in the Baltic Sea during the period 1900-2020.

This paper presents a method for synthesizing mobile laser scanning point clouds of railroad level crossings that can be used to train neural networks for point cloud segmentation. The method arranges point cloud samples representing individual objects into new scenes using a set of simple placement rules. The point cloud samples can be cropped from real point clouds, created from 3D mesh models, or procedurally generated using mathematical functions. The scenes can consist of one or more types of samples, making it possible to combine real and synthetic data. The findings show that a network trained on scenes generated from real point cloud samples resulted in a better overall F1-score compared to a network that was trained using real scenes. Also, the performance of a network trained on a very small amount of real scenes can be improved by adding fully synthetic scenes to the training data.

Capturing geographic information from a mobile platform, a method known as mobile mapping, is today one of the best methods for rapid and safe data acquisition along roads and railroads. The digitalization of society and the use of information technology in the construction industry is increasing the need for structured geometric and semantic information about the built environment. This puts an emphasis on automatic object identification in data such as point clouds. Most point clouds are accompanied by RGB images, and a recent literature review showed that these are possibly underutilized for object identification. This article presents a method (image-based point cloud segmentations - IBPCS) where semantic segmentation of images is used to filter point clouds, which drastically reduces the number of points that have to be considered in object identification and allows simpler algorithms to be used. An example implementation where IBPCS is used to identify roadside game fences along a country road is provided, and the accuracy and efficiency of the method is compared to the performance of PointNet, which is a neural network designed for end-to-end point cloud classification and segmentation. The results show that our implementation of IBPCS outperforms PointNet for the given task. The strengths of IBPCS are the ability to filter point clouds based on visual appearance and that it efficiently can process large data sets. This makes the method a suitable candidate for object identification along rural roads and railroads, where the objects of interest are scattered over long distances.

Monitoring deformation of man-made structures is very important to prevent them from a risk of collapse and save lives. Such a process is also used for monitoring change in historical objects, which are deforming continuously with time. An example of this is the Vasa warship, which was under water for about 300 years. The ship was raised from the bottom of the sea and is kept in the Vasa museum in Stockholm. A geodetic network with points on the museum building and the ship's body has been established and measured for 12 years for monitoring the ship's deformation. The coordinate time series of each point on the ship and their uncertainties have been estimated epoch-wisely. In this paper, our goal is to statistically analyse the ship's hull movements. By fitting a quadratic polynomial to the coordinate time series of each point of the hull, its acceleration and velocity are estimated. In addition, their significance is tested by comparing them with their respective estimated errors after the fitting. Our numerical investigations show that the backside of the ship, having highest elevation and slope, has moved vertically faster than the other places by a velocity and an acceleration of about 2 mm/year and 0.1 mm/year(2), respectively and this part of the ship is the weakest with a higher risk of collapse. The central parts of the ship are more stable as the ship hull is almost vertical and closer to the floor. Generally, the hull is moving towards its port and downwards.