AN APPLICATION OF CADASTRAL FABRIC SYSTEM IN IMPROVING POSITIONAL ACCURACY OF CADASTRAL DATABASES IN MALAYSIA

Cadastral fabric is perceived as a feasible solution to improve the speed, efficiency and quality of the cadastral measurement data to implement Positional Accuracy Improvement (PAI) and to support Coordinated Cadastral System (CCS) and Dynamic Coordinated Cadastral System (DCCS) in Malaysia. In light of this, this study aims to propose a system to upgrade the positional accuracy of the existing cadastral system through the utilisation of the cadastral fabric system. A comprehensive investigation on the capability of the proposed system is carried out. A total of four evaluation aspects is incorporated in the study to investigate the feasibility and capability of the software, viz. performance of geodetic least squares adjustment, quality assurance techniques, supporting functions, and user friendliness. This study utilises secondary data obtained from the Department of Surveying and Mapping Malaysia (DSMM). The test area is coded as Block B21701 which is located in Selangor, Malaysia. Results show that least square adjustment for the entire network is completed in a timely manner. Various quality assurance techniques are implementable, namely error ellipses, magnitude of correction vectors and adjustment trajectory, as well as inspection of adjusted online bearings. In addition, the system supports coordinate versioning, coordinates of various datum or projection. Last but not least, user friendliness of the system is identified through the software interface, interaction and automation functions. With that, it is concluded that the proposed system is highly feasible and capable to create a Cadastral Fabric to improve the positional accuracy of existing cadastral system used in Malaysia.

Robust Estimation Method Based on Function Model of Successive Approximation

In measurement practice, the residuals in least squares adjustment usually show various abnormal discrete distributions, including outliers, which is not conducive to the optimization of final measured values. Starting with the physical mechanism of dispersion and outlier of repeated observation errors, this paper puts forward the error correction idea of using the approximate function model of error to approach the actual function model of error step by step, gives a new theoretical method to optimize the final measured values, and proves the effectiveness of the algorithm by the ability of responding to the true values. This new idea is expected to be the ultimate answer of robust estimation theory.

The Celestial Frame and the Weighting of the Celestial Pole Offsets in the Computation of VLBI-Based Corrections for the Main Lunisolar Nutation Terms

Very long baseline interferometry (VLBI) is the only technique in space geodesy that can determine directly the celestial pole offsets (CPO). In this paper, we make use of the CPO derived from global VLBI solutions to estimate empirical corrections to the main lunisolar nutation terms included in the IAU 2006/2000A precession–nutation model. In particular, we pay attention to two factors that affect the estimation of such corrections: the celestial reference frame used in the production of the global VLBI solutions and the stochastic model employed in the least-squares adjustment of the corrections. In both cases, we have found that the choice of these aspects has an effect of a few μas in the estimated corrections.

Automated near-field deformation detection from mobile laser scanning for the 2014 Mw 6.0 South Napa earthquake

Quantifying off-fault deformation in the near field remains a challenge for earthquake monitoring using geodetic observations. We propose an automated change detection strategy using geometric primitives generated using a deep neural network, random sample consensus and least squares adjustment. Using mobile laser scanning point clouds of vineyards acquired after the magnitude 6.0 2014 South Napa earthquake, our results reveal centimeter-level horizontal ground deformation over three kilometers along a segment of the West Napa Fault. A fault trace is detected from rows of vineyards modeled as planar primitives from the accumulated coseismic response, and the postseismic surface displacement field is revealed by tracking displacements of vineyard posts modeled as cylindrical primitives. Interpreted from the detected changes, we summarized distributions of deformation versus off-fault distances and found evidence of off-fault deformation. The proposed framework using geometric primitives is shown to be accurate and practical for detection of near-field off-fault deformation.

Least Squares Adjustment with a Rank-Deficient Weight Matrix and Its Applicability to Image/Lidar Data Processing

This article proposes a solution to special least squares adjustment (LSA) models with a rank-deficient weight matrix, which are commonly encountered in geomatics. The two sources of rank deficiency in weight matrices are discussed: naturally occurring due to the inherent characteristics of LSA mathematical models and artificially induced to eliminate nuisance parameters from LSA estimation. The physical interpretation of the sources of rank deficiency is demonstrated using a case study to solve the problem of 3D line fitting, which is often encountered in geomatics but has not been addressed fully to date. Finally, some geomatics-related applications—mobile lidar system calibration, point cloud registration, and single-photo resection—are discussed along with respective experimental results, to emphasize the need to assess LSA models and their weight matrices to draw inferences regarding the effective contribution of observations. The discussion and results demonstrate the vast applications of this research in geomatics as well as other engineering domains.

Assessment of Adjustments Methods in Traverse Networks for Positioning

Various factors contribute to the degree of accuracy of the adjusted parameter (coordinate), one of which is the choice of adjustment model. Adjustment models seeks to eliminate (accounts) for the presence of random errors present in a given observations. The choice is critical for surveyors and other spatial analysts for optimal positioning and mapping projects since different adjustment models will yield different level of accuracy of spatial information generated irrespective of the quality of observations. For a traversing network, various adjustment models have been put forward which include; the Transit, the Bowditch, and the Crandels models. In spite of these models, internal consistency and reliability indicators of the network of positions are determined using the least squares adjustment model (observation equation and condition equation models). The aim of this work is to analyze the various traverse adjustment models. The approach deployed in this work was to compute the provisional coordinate of six traverse stations using the approximate methods of adjustment i.e., Bowditch and transit methods of traverse adjustment models. In addition, the least square adjustment models were deployed to minimize the propagation of residuals of the obtained values. The adjusted distances and directions were then compared with the observed distances and directions to obtain the residuals. The coordinate of positions was determined and the Root Mean Square Error (RMSE) associated with the traverse adjustment models are given as 0.128702264 and 0.008560954. Similarly, the RMSE of the adjusted values using the least square models are given as 0.007181432, and 0.005763969 for the observation and condition equation models respectively. The analysis of these results reveals that the traverse adjustment models are unique with capabilities embedded in the determination of the observables during data acquisition. However, for mapping and engineering survey of small locations, the transit method is more preferable to the Bowditch method.