No abstract

The transformation of coordinates allows for the conversion of coordinates from one geodetic system to another. Usually, the determination of transformation parameters is performed by the means of a least squares method. Unfortunately, the least squares method is not immune to outliers. It means that if, for any reason, some reference points are disturbed with gross errors or they belong to two different archival coordinate systems, transformation parameters will be estimated with those errors. Therefore, it is very important to identify incorrect data and remove them from the estimation process or decrease their influence on the estimated parameters. This problem can be solved by applying the M split estimation to calculate transformation parameters. The method of estimation adopted in the paper allows the determination of two competitive vectors of transformation parameters and two competitive residual vectors. The suitability of using the M split estimation method in the process of coordinate transformation was tested on a real geodetic network. In the 'M split estimation' section the authors presents the idea of M split estimation, along with its application to estimation of transformation parameters. The authors performed the calculations in three scenarios: with different number, value and distribution of gross errors respectively. The results of the transformations compared with the catalogue value of coordinates, as well as the differences between coordinates after Helmert transformation, the M split transformation (for the vector of parameters V a ), the M split transformation (for the vector of parameters V b ) and the catalogue coordinates are presented in the 'Numerical example' section.

Historic buildings, due to their architectural, cultural, and historical value, are the subject of preservation and conservatory works. Such operations are preceded by an inventory of the object. One of the tools that can be applied for such purposes is Light Detection and Ranging (LiDAR). This technology provides information about the position, reflection, and intensity values of individual points; thus, it allows for the creation of a realistic visualization of the entire scanned object. Due to the fact that LiDAR allows one to ‘see’ and extract information about the structure of an object without the need for external lighting or daylight, it can be a reliable and very convenient tool for data analysis for improving safety and avoiding disasters. The main goal of this paper is to present an approach of automatic wall defect detection in unlit sites by means of a modified Optimum Dataset (OptD) method. In this study, the results of Terrestrial Laser Scanning (TLS) measurements conducted in two historic buildings in rooms without daylight are presented. One location was in the basement of the ruins of a medieval tower located in Dobre Miasto, Poland, and the second was in the basement of a century-old building located at the University of Warmia and Mazury in Olsztyn, Poland. The measurements were performed by means of a Leica C-10 scanner. The acquired dataset of x, y, z, and intensity was processed by the OptD method. The OptD operates in such a way that within the area of interest where surfaces are imperfect (e.g., due to cracks and cavities), more points are preserved, while at homogeneous surfaces (areas of low interest), more points are removed (redundant information). The OptD algorithm was additionally modified by introducing options to detect and segment defects on a scale from 0 to 3 (0—harmless, 1—to the inventory, 2—requiring repair, 3—dangerous). The survey results obtained proved the high effectiveness of the modified OptD method in the detection and segmentation of the wall defects. The values of area of changes were calculated. The obtained information about the size of the change can be used to estimate the costs of repair, renovation, and reconstruction.

Detection of bio-deterioration and moisture is one of the most important tasks for comprehensive diagnostic measurements of buildings and structures. Any undesirable change in the material properties caused by the action of biological agents contributes to gradual aesthetic and physical damage to buildings. Very often, such surface changes can lead to structural defects or poor maintenance. In this paper, radiometric analysis of point clouds is proposed for moisture and biofilm detection in building walls. Recent studies show that remote terrestrial laser scanning (TLS) technology is very useful for registering and evaluating the technical state of the deterioration of building walls caused by moisture and microorganisms. Two different types of TLS, time-of-flight and phase-shift scanners, were used in the study. The potential of TLS radiometric data for detecting moisture and biofilm on wall surfaces was tested on two buildings. The main aim of the research is to compare two types of scanners in the context of their use in the detection of moisture and microorganisms.

Over the years there have been a number of different computational methods that allow for the identification of outliers. Methods for robust estimation are known in the set of M-estimates methods (derived from the method of Maximum Likelihood Estimation) or in the set of R-estimation methods (robust estimation based on the application of some rank test). There are also algorithms that are not classified in any of these groups but these methods are also resistant to gross errors, for example, in M-split estimation. Another proposal, which can be used to detect outliers in the process of transformation of coordinates, where the coordinates of some points may be affected by gross errors, can be a method called RANSAC algorithm (Random Sample and Consensus). The authors present a study that was performed in the process of 2D transformation parameter estimation using RANSAC algorithm to detect points that have coordinates with outliers. The calculations were performed in three scenarios on the real geodetic network. Selected coordinates were burdened with simulated values of errors to confirm the efficiency of the proposed method. Keywords: Coordinate Transformation; RANSAC; Parameter Estimation. RESUMO