Reliability of using LS-VCE computation in Deriving Variances for Multi-Classes Dataset

Abstract

Abstract Stochastic modelling (SM) plays an essential role in least-squares adjustment (LSA), especially for geodetic network data processing. Estimated variances derived from SM are vital factors in determining the reliability of the computed parameter vectors and ensuring the sensitivity of adjustment outcomes toward outliers. As there are multi-source of datasets consisting various of data quality, there is still room for improvement when positional accuracy becomes the main priority. Concerning the accuracy argument, legacy datasets that were exploited in establishing the National Digital Cadastral Database (NDCDB) were obtained from multi-classes of measurement (i.e., first, second and third classes). Taking into account this condition, this research has investigated the capability of stochastic modelling to preserve the positional accuracy of land records that comprehends from multi-classes data quality. To achieve that, the algorithm of Least Squares Variance Component Estimator (LSVCE) was employed in estimating realistic variances. First and second classes measurement were yielded from three (3) certified plans (CPs) which are CP93887, CP80333, and CP33758. Comparison between the adjusted results computed from the combined and separated variance according to data classes have demonstrated that combined variance can detect outliers while separated variance can give realistic adjustment results. From these outcomes, the experiments verified that a hybrid solution is needed for both data classes in order to preserve positional accuracy. In conclusion, to ensure the accuracy of survey data in the future, a proper variance component is needed to improve the coordinated cadastral database.