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

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