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).

The paper presents inspection and assessment of as-built reinforcement of a selected segment of an incrementally launched (IL) concrete bridge under construction. Two novel techniques were used for data acquisition: modified photogrammetry and High Definition Surveying - a combination of terrestrial-based laser scanning, computer technology and precision control networks. The main goal of this in-situ experiment was to develop a practical, effective, yet affordable methodology for inspecting reinforcement of the above mentioned structures. In order to maintain the requirements resulting from the specifics of IL technology (seven-day cycles, 24/7 operation, two concrete pours per week), the authors have limited the maximum allowed time for data acquisition, minimized the complexity of measurement data processing and related requirements for the software and computers, and replaced the professional equipment for photogrammetry with a commonly available SLR camera. Data obtained using this method was than referred to the point cloud model obtained with a precise, state-of-the-art 3D laser scanner. The adopted mathematical model for data post-processing turned out to be effective and correct both for the analysis of data from laser scanners and photogrammetry. Presented solution adopts practical workflow with camera calibration to simplify in-situ measurement procedure achieving high accuracy standards required for reinforcement inspection. The point cloud data model obtained from photogrammetry turned out to be insufficient to assess web reinforcement thoroughly. In turn, it was possible to inspect and evaluate the reinforcement of the bottom slab, with accuracy matching the laser scanning.

With increasing access to open spatial data, it is possible to improve the quality of analyses carried out in the preliminary stages of the investment process. The extraction of buildings from raster data is an important process, especially for urban, planning and environmental studies. It allows, after processing, to represent buildings registered on a given image, e.g., in a vector format. With an actual image it is possible to obtain current information on the location of buildings in a defined area. At the same time, in recent years, there has been huge progress in the use of machine learning algorithms for object identification purposes. In particular, the semantic segmentation algorithms of deep convolutional neural networks which are based on the extraction of features from an image by means of masking have proven themselves here. The main problem with the application of semantic segmentation is the limited availability of masks, i.e., labelled data for training the network. Creating datasets based on manual labelling of data is a tedious, time consuming and capital-intensive process. Furthermore, any errors may be reflected in later analysis results. Therefore, this paper aims to show how to automate the process of data labelling of cadastral data from open spatial databases using convolutional neural networks, and to identify and extract buildings from high resolution orthophotomaps based on this data. The conducted research has shown that automatic feature extraction using semantic ML segmentation on the basis of data from open spatial databases is possible and can provide adequate quality of results.

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.

This article concerns the evaluation of displacement monitoring referred to the earth-filled flood embankment using surveying and radar measurement techniques. The results allow to reveal the embankment reaction to the external loads. The results of long-term geodetic monitoring clearly imply the dominance of displacements directed outside for points located on the embankment slopes. The displacement values were also referred to the course of the changing water level representing a flood wave, in order to point out the mutual relationship between them. Horizontal displacements indicate that earth-filled embankments may behave to some extent like elastic structures. For the experiment, which covered determining the embankment response to the varying water level, ground-based radar interferometry was used as an alternative measurement technique. This selection was justified primarily by the submillimeter accuracy of the displacement measurement. However, the research required overcoming a few limitations, among which the atmosphere variability is the most important. This was taken into account by measuring the current atmospheric conditions and correcting the results by atmospheric delay. For the time intervals in which it was possible to compare surveying and radar measurement techniques, the displacement values were analyzed and referred to the current structure load resulting from the variable water level. A sizable number of the observations allow to perceive some tendencies, even if the displacement values were at the measurement uncertainty level. The movement trend is consistent for both methods. This was evidenced particularly by the compatibility of the considerable displacements detected for the points located in the area of presumed embankment failure. Moreover, the differences are within the limits of agreements determined on the basis of the Bland–Altman plot, which means that these two measurement methods could be used interchangeably.