Journal of Surveying Engineering

Indoor high-precision maps are necessary for many applications, including robot navigation and location-based services. Raw indoor map boundaries are too irregular and coarse to be used in practical applications directly; thus, correction for indoor map boundaries is necessarily carried out. Considering that least-square (LS) methods cannot process the errors in a coefficient matrix, we proposed a normalized total least-squares of condition (NTLSC) equation method to solve for polylines. The proposed NTLSC is more robust than LS with respect to the ill-posed problem in iteration, and linearization need not be employed, which simplifies the complexity of the formula. Aiming at the curves in the map, an iterative LS (ILS) strategy was designed to rectify it for high precision. However, due to the use of different correction models, there will be some tiny gaps between the curve and polylines. Therefore, a junction processing method was put forward, which is an indispensable step to preserve the integrality of the indoor map boundary. Finally, the indoor map boundaries of two scenes were refined by the proposed method, and the results of two perspectives of qualitative and quantitative evaluations indicate that the proposed method can effectively correct irregular and coarse map boundaries.

The mixed additive and multiplicative random error model is a combination of the additive error random model and multiplicative random error model. Weakness is an attribute of the mixed additive and multiplicative random error model, and the ill-posed problem of the coefficient matrix is ignored in the existing parameter estimation methods for addressing the model, which will result in an unstable or nonconvergent solution. Aiming at solving this problem, this paper first derives the virtual observation iterative solution (VOIS) formula for the ill-posed mixed additive and multiplicative random error model by combining the observation equation of the mixed additive and multiplicative random error model and the virtual observation equation. Furthermore, based on the principle of the A-optimal design, the A-optimal design method is proposed to determine the regularization parameter of the ill-posed model. Finally, the VOIS method is applied in simulated and actual data for verification and analysis and is compared with existing methods. The experimental results show that the A-optimal design method can determine reasonable regularization parameters and that the VOIS method can obtain more accurate parameter estimations than existing methods and has strong feasibility and applicability.

A disproportionate number of serious traffic accidents caused by lane offset occur at horizontal curvatures. Widely used methods for vehicle trajectory measurement, such as the vehicle position system and unmanned aerial vehicle (UAV), are unsuitable for wheel lane offset detection due to the resolution of data and shooting scope of cameras, respectively. To evaluate the lane offset risk of horizontal curvatures, automatic methods are proposed for horizontal alignment and vehicle trajectory measurement using a roadway inspection system (RIS), integrating a binocular stereo vision measurement system, initial measurement unit, and global positioning system (GPS). A mask region-based convolutional neural network (R-CNN) model is applied to detect lane markings. Based on binocular stereo vision technology, the lane offset of a vehicle on horizontal curvatures is measured continuously. To investigate the impacts of horizontal alignments on lane offset, inertial measurement unit (IMU) data and road scene images are applied for horizontal alignment measurement, including point of curve (PC) and point of tangent (PT) stations, curve length, curve radius, and turning direction. Four one-lane horizontal curvatures on highway ramps are selected as a test bed. Based on field data, the impact of horizontal alignments on lane offset is analyzed, and hazardous locations with lane offset risks are detected. This study can facilitate traffic safety analysis and the horizontal alignment design of roadway curvatures.

The progress of a typhoon can be investigated by analyzing the significant changes in precipitable water vapor (PWV) in the area of interest. In this study, PWV variations of two typhoons in 2018 were investigated based on global navigation satellite system (GNSS)–derived PWV (GNSS-PWV) for predicting typhoon paths. In the study area, GNSS stations are densely distributed like an array so that the array enables the monitoring of the variations of PWV over the region more sensibly than a conventional PWV monitoring method, which consists of the radiosonde-based PWV observations (RS-PWV). To predict a typhoon’s subsequent location, the research proposes the concept of predicted location of typhoon (PLT). PLT is calculated as follows: (1)  all the GNSS-PWV in the study area during a typhoon event is estimated; (2) the top five GNSS stations showing the highest PWV are selected; (3) their two-dimensional (2D) mean position is calculated; and (4) this position indicates the PLT corresponding to the progress of the typhoon. The PLT was able to predict a typhoon’s subsequent location approximately 5 h in advance with an average distance discrepancy of 14 km. When the typhoon approached the landfall point, the PLT managed to predict the point approximately 5.3 h in advance, with an average distance discrepancy from the actual path of 13.19 km. These results imply that GNSS-PWV is promising for the prediction of the typhoon path, and it can be used as supplementary information for the current typhoon forecast.

Weighted total least-squares (WTLS) adjustment is a rigorous method used for estimating parameters in the errors-in-variables (EIV) model. However, its computational efficiency is limited due to the large number of matrix operations involved, which are extremely time-consuming, particularly when processing large data sets. Based on the structural characteristics of the EIV model, the design matrix is divided into a constant matrix and a random matrix. Then the EIV model is rewritten as a general structured model and reformulate it as an efficient WTLS algorithm, which only attaches a weight matrix to the random matrix to reduce the size of the matrices involved in the iterative process. In addition, the proposed algorithm does not reestimate the random matrix in each iteration. All of this helps to improve computational efficiency. Numerical results confirm that the proposed algorithm can obtain the same accuracy as other existing improved algorithms, but using the same hardware, which requires significantly less time and memory.

This study investigated the rate of natural subsidence along the Texas coast using multidecadal to century tide gauge (TG) and global positioning system (GPS) data sets. The rates of land subsidence and sea level rise are aligned to the Gulf of Mexico (GOM) Reference Frame 2020 (GOM20), which is tied to the stable portion of the Gulf Coastal Plain. GOM20 provides a robust reference for ruling out regional ground movements associated with regional tectonics and glacial isostatic adjustment (GIA) and highlighting natural subsidence in the Gulf Coast Aquifer region. According to this study, the mean sea level rise rate within the GOM was $2.6\mathrm{mm}/\mathrm{year}$ with respect to GOM20 from the 1970s to the 2010s. Present land subsidence along the Texas coast is dominated by the natural subsidence varying from $0.7\mathrm{mm}/\mathrm{year}$ in the central coastal area (Port Mansfield, Corpus Christi, and Rockport) to $1.6\mathrm{mm}/\mathrm{year}$ in the southern coastal area (South Padre Island) to $1.5–3.5\mathrm{mm}/\mathrm{year}$ in the northern coastal area (Freeport, Galveston Island, Texas City, and Sabine Pass). The average natural subsidence rate along the 600-km Texas coastline is $1.4\mathrm{mm}/\mathrm{year}$ with respect to GOM20. Four scenarios (lowest, medium-low, medium-high, and highest) for future coastal submergence were developed by integrating the natural subsidence and sea level rise along the Texas coast with the global sea level scenarios. Our analysis projects that the average submergence along the Texas coastline from 2020 to 2100 will be greater than 0.3 m, and likely between 0.6 and 1.2 m, but is unlikely to exceed 2.0 m.

The accurate determination of deflections of the vertical is a complex scientific task that can be time-consuming and expensive. On occasion, good approximations are required for surveying and engineering applications. Although deflections of the vertical derived from geoid models are available online in certain regions, the exact computation procedure is not detailed and they generally are given without accompanying uncertainties. This work provides a simple, straightforward way, using least squares, to determine deflections of the vertical at geoid heights and their corresponding stochastic quantities, all based on undulations of the EGM2008 geoid model selected around the computation point. Three case studies were discussed, and an ancillary sample program is attached for completeness.

Sea surface height (SSH), a measurement widely used in marine science, can be used to compute the marine gravity field while providing underlying information on the ocean current, tide, and geoid. A traditional SSH measurement relies on tide stations and satellite altimetry. Shipborne SSH measurements not only alleviate the influence of poor nearshore waveforms on satellite altimetry reliability but also enable large-scale surveys. Moreover, it is favored by the high sampling rate and superior spatial resolution. Precise point positioning (PPP) allows operations independent of the land-based station, facilitating flexibility and efficiency. Accordingly, PPP is used to determine the ellipsoid height based on shipborne GPS data. The PPP computations are performed using the Canadian Spatial Reference System (CSRS)–PPP, GrafNav, and Bernese. The results of the CSRS-PPP have better accuracy and are easy to use. The corrections of the height difference between a GPS antenna and sea surface, earth tide, ocean tide, and filtering are tested to obtain an accurate SSH measurement. The corrected SSH accuracy is improved from 206.2 to 22.9 cm based on a crossover analysis. Through the adjustment of the crossover differences, the result shows an accuracy of 7.5 cm. The comparison with the DTU18 mean sea surface (MSS) model shows that the standard deviation of the differences is 21.9 and 11.9 cm for the corrected SSH before and after the adjustment, respectively. The adjusted SSH shows an obvious improvement of 62.2% and 32.8% in the standard deviation of the crossover differences and the differences with the DTU18 MSS model.

The proposed voxel change (VC) algorithm provides accurate, scalable, and quantifiable change detection for urban aerial Light Detection and Ranging (LiDAR) scans. This VC algorithm uses MapReduce, a big data programming model, to map neighboring points into cubes. The algorithm converts each data set into a group of cubes, and classifies them into categories of building, ground, or vegetation. It then compares and quantifies changes in area or volume. Spatial discontinuity is overcome by clustering. Quality metrics are demonstrated by comparing a $1{\mathrm{km}}^2$ data set of Dublin, Ireland, using a 2007 scan with a point density of 225 points per square meter ($\mathrm{pts}/{\mathrm{m}}^2$) and a 2015 scan with $335\mathrm{pts}/{\mathrm{m}}^2$ (totaling more than 500 million points). By using only positional LiDAR information as the data input, the quality metric exceeded 90% across the full data set with respect to lost, new, and unchanged designations for vegetation, buildings, and ground areas, and regularly exceeded 98% for buildings. The technique successfully processes nonrectilinear features and robustly provides a quantification of change for both building expansion and vegetation at a $1{\mathrm{m}}^3$ level using dense, modern data sets.

Most of the human-made infrastructures (e.g., dams) need very precise geodetic networks and constant monitoring to detect risks of failure and to plan civil engineering maintenance works. The combination of different measurements helps in determining displacements with high precision; therefore, the risk of damages is reduced. In this paper, we present a new approach, which considers a special geodetic observation strategy as a method to significantly reduce the volume of operations of a precise geodetic network and changes the designing concept of such networks. Decrease in data collection time and cost while keeping or increasing the quality of control networks has been one of the most important goals of any network designer. This paper proposes a method exploiting network properties to convert slope distances to the horizontal ones to be used in the classic terrestrial geodetic two-dimensional (2D) networks. We have evaluated the proposed method in different dam geodetic control networks in Iran. The network adjustment results show the acceptable performance of the presented method compared with the methods that are currently in use.

This paper introduces Version 0.3 of the TRANS4D software, where TRANS4D is short for Transformations in Four Dimensions. TRANS4D enables geospatial professionals and others to transform three-dimensional (3D) positional coordinates across time and among several popular terrestrial reference frames. Version 0.3 includes a crustal velocity model for a neighborhood of the Caribbean plate in the form of 3D crustal velocity estimates at the nodes of a two-dimensional (grid in latitude and longitude. This velocity model supplements existing TRANS4D velocity models for the continental United States and for parts of Alaska and Canada. This paper also introduces a terrestrial reference frame, called the Caribbean Terrestrial Reference Frame of 2014 (CATRF2014), which was derived from horizontal crustal velocities for 25 geodetic stations. These stations are considered to be “stable” relative to one another, because each has a horizontal velocity whose magnitude is less than $1.0\mathrm{mm}/\mathrm{year}$ relative to CATRF2014. This new reference frame is defined in terms of a three-parameter transformation from the International Global Navigation Satellite System Service 2014 (IGS14) reference frame, which can be considered identical to the International Terrestrial Reference Frame of 2014 (ITRF2014). These three parameters correspond to the Euler-pole parameters that hopefully quantify the motion of the stable interior of the Caribbean plate. However, the location of this stable interior is not well known because most of it resides underwater.

In the last decade, a number of correction services for global navigation satellite systems (GNSS) precise positioning have been developed, mainly for offshore applications, based on a precise point positioning (PPP) real-time processing. These allow receiving corrections without the need for an internet connection or reference benchmarks around the survey area. In the paper, we tested the Atlas correction service implemented in a Stonex S900A machine (Monza, Italy), with the purpose to verify its performances under optimal operational conditions and in the practical case of a land survey on several benchmarks along the Adriatic coast. The data analysis focused on: accuracy with respect to the reference frame, repeatability of the coordinates considering short and long acquisition periods, time to initialize the survey, and reliability of the formal errors provided by the instrument. The system confirmed the declared performances in most cases and is shown to be a viable alternative to other GNSS techniques also for land surveys where no obstacles affect the sky visibility.