Positioning with low-cost GNSS (Global Navigation Satellite System) receivers is becoming increasingly popular in many engineering applications. In particular, dual-frequency receivers, which receive signals of all available satellite systems, offer great possibilities. The main objective of this research was to evaluate the accuracy of a position determination using low-cost receivers in different terrain conditions. The u-blox ZED-F9P receiver was used for testing, with the satellite signal supplied by both a dedicated u-blox ANN-MB-00 low-cost patch antenna and the Leica AS10 high-precision geodetic one. A professional Leica GS18T geodetic receiver was used to acquire reference satellite data. In addition, on the prepared test base, observations were made using the Leica MS50 precise total station, which provided higher accuracy and stability of measurement than satellite positioning. As a result, it was concluded that the ZED-F9P receiver equipped with a patch antenna is only suitable for precision measurements in conditions with high availability of open sky. However, the configuration of this receiver with a geodetic-grade antenna significantly improves the quality of results, beating even professional geodetic equipment. In most cases of the partially obscured horizon, a high precision positioning was obtained, making the ZED-F9P a valuable alternative to the high-end geodetic receivers in many applications.

This article describes a case of using remote sensing during a static load test of a large bridge, which, because of its location, belongs to a critical city infrastructure. The bridge in question is the longest tram flyover in Poland. This is an extradosed-type concrete structure. It conducts a long tram line over 21 other active lines of an important railway station in the center of Cracow. The diagnostic of such bridges involving the load test method is difficult. Traditional, contact measurements of span displacements are not enough anymore. In such cases, remote sensing becomes an indispensable solution. This publication presents an example of using the close-range radar remote sensing technique of ground-based radar interferometry. However, the cross-sections of the huge bridge were observed using several methods. The aim was to confirm the conditions and efficiency of radar displacement measurements. They were therefore traditional contact measurements using mechanic sensors conducted, if possible, to the bottom of the span, for precise leveling and measurement using electronic total station. Comparing the results as well as the discussion held demonstrated the fundamental advantages of remote sensing methods over the other more traditional techniques.

Ground-based radar interferometry (GBSAR) is a useful method to control the stability of engineering objects and elements of geographical spaces at risk of deformation or displacement. To secure accurate and credible measurement results, it is crucial to consider atmospheric conditions as they influence the corrections to distance measurements. These conditions are especially important considering the radar bandwidth used. Measurements for the stability of engineering objects are not always performed in locations where meteorological monitoring is prevalent; however, information about the range of variability in atmospheric corrections is always welcome. The authors present a hybrid method to estimate the probable need of atmospheric corrections, which allows partly eliminating false positive alarms of deformations as caused by atmospheric fluctuations. Unlike the numerous publications on atmospheric reductions focused on the current state of the atmosphere, the proposed solution is based on applying a classic machine learning algorithm designed for the SARIMAX (Seasonal Autoregressive Integrated Moving Average with covariate at time) time series data model for satellite data shared by NASA (National Aeronautics and Space Administration) during the Landsat MODIS (Moderate Resolution Imaging Spectroradiometer) mission before performing residual estimation during the monitoring phase. Example calculations (proof of concept) were made for ten-year satellite data covering a region for experimental flood bank stability observations as performed using the IBIS-L (Image by Interferometric Survey—Landslide) radar and for target monitoring data (ground measurements).

This paper details the development of a camera calibration method purpose-built for use in photogrammetric survey production. The calibration test field was established in a hangar, where marker coordinates were measured using a high-precision survey methodology guaranteeing very high accuracy. An analytical model for bundle adjustment was developed that does not directly use the coordinates of field calibration markers but integrates bundle adjustment and survey observations into a single process. This solution, as well as a classical calibration method, were implemented in a custom software, for which the C++ source code repository is provided. The method was tested using three industrial cameras. The comparison was drawn towards a baseline method, OpenCV implementation. The results point to the advantages of using the proposed approach utilizing extended bundle adjustment.

This paper presents the practical results of the evaluation of the data obtained by using ground‐based radar interferometer during measurements carried out on bridge structures. Due to the nature of the objects studied, the authors proposed a comprehensive method of data analysis, which identifies whether the passage of the vehicle did not damage the bridge. The effective use of vehicles as a source of bridge excitation allowed us to first develop a method for determining the damping parameters resistant to potentially occurring beating frequencies. As a result, it is possible to determine these subsets of data registered with radar, for which it is possible to assume compliance with linear systems. This type of data, often omitted in other works, forms the basis for the second important element of the research—an algorithm based on the ARMA model supporting defect detection. The optimization of the performed calculations, in particular the proposed optimal ARMA model order, the method of fault identification based on the DSF parameter, or fault identification based on a nonmetrical Cook’s distance leads to a robust and scalable method. The method’s low computational complexity allows for implementation in real‐time solutions. In addition, the distribution of errors and the sensitivity of classifiers based on the DSF parameter and Cook’s distances leaving them will enable the automation of the classification process using machine learning. The proposed method is universal; in particular, it can be used for radar interferometry methods because it is resistant to potentially variable environmental conditions.