The demand for positional accuracy and multi-dimensional data have demonstrated drastic changes in the geomatics data adjustment approach. Furthermore, the capability of modern sensors to provide high accuracy data (i.e., global navigation satellite system) has caused the crucial requirement for a rigorous adjustment that can process data from multi-sensors. Geomatics practitioners have gradually transformed the adjustment procedure to the most rigorous approach (i.e., parametric linear regression) to adapt to current demand. However, legacy datasets that utilize independent line constraint in the traditional adjustment approach have caused significant uncertainties in parametric linear regression (LR) adjustment. To resolve this dilemma, this research has designed robust experiments using closed traverse types: single-line constraint, multi-line constraints, and sub-network line constraint. Through errors trend and network form deterioration analyses, the outcomes have visually and numerically verified the insignificant of independent line constraints in parametric LR. However, the establishment of control points at the beginning or end of lines could solve the limitation of the abovementioned issue. In both analyses, control points at initial lines have demonstrated the best solution for constrained adjustment. The obtained results have exemplified the appropriate implementation of network adjustment in the presence of line constraints. As positional accuracy becomes the main priority, it can be concluded that points-based constraints are more advisable in preserving the quality of cadastral network adjustment.
Analysis of Charlevoix precise gravity data from 1976 to 1984: details of a precise gravity network adjustment procedure
