Journal of Surveying Engineering

The equivalency of observations in geodetic networks is one of the most important problems in surveying engineering, which has not been seriously considered before. This means that different types of observations should, on average, be equivalent from an accuracy and reliability point of view. In this paper, a method for the second-order design problem of geodetic networks to meet equivalent accuracy and reliability for different groups of observations has been suggested. In this technique, the weights of observations have been improved in such a way that the same average redundancy numbers are obtained for all types of observations. The efficiency of the methodology has been verified by numerical results.

An extended parcel-based land information system (LIS) was designed and implemented with respect to six additional major functional requirements: (1) incorporation of measurement data including measurement data retrieval, automatic coordinate update, and spatial data consistency checking; (2) identification of spatial discrepancy; (3) retention of historical data; (4) temporal topology; (5) snapshot; and (6) incorporation of document image data. To support the requirements, a spatio-temporal data model is proposed. In addition, associated functions—least squares adjustment, blunder detection, automatic coordinate update, and geometric process for identifying spatial discrepancies—are presented for the extended parcel-based LIS. The proposed data model and functions were implemented using Prolog, XPCE, and C++ and tested with cadastral data in Dane County, Wisconsin. The system is titled the Temporal Land Information System (TLIS).

The nature of cadastral data in the United States is dynamic from a temporal perspective and measurement-based from a spatial perspective. The fundamental characteristics have been comparatively ignored in the development of early parcel-based land information systems (LISs). Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in six target groups: (1) surveyors; (2) title insurance workers; (3) the register of deed office; (4) property assessors; (5) planners; and (6) geographic information system application developers and users. The investigation ended up with six major functional requirements: (1) incorporation of measurement data including measurement data retrieval, automatic coordinate update, and spatial data consistency checking; (2) identification of spatial discrepancy; (3) retention of historical data; (4) temporal topology; (5) snapshot; and (6) incorporation of document image data. The writer presents the preceding functional requirements in order to extend the capability of current parcel-based LISs and to support more users’ needs.

Stochastic modeling of the global positioning system (GPS) residual errors yields a fully populated covariance matrix for the GPS carrier phase double difference observations. Implementing this fully populated covariance matrix into a software package usually slows down the numerical computations. This, however, is not the case if an exponential function can be used to approximate the actual covariance function of the GPS residual errors. Using the exponential function results in a block diagonal weight matrix for the double-difference observations. In this paper, an algorithm for the efficient computation of the inverse of this fully populated covariance matrix is developed. Both the ideal case of tracking the same satellites over time as well as the more realistic case of tracking different satellites over time are considered. The storage requirement for the adjustment process is also discussed.

In China, normal height, which is the height above the geoid calculated using the mean normal gravity along the plumb line, is used in engineering applications. However, the adjusted global positioning system (GPS) height is the height above the surface of the WGS-84 ellipsoid. Thus, it is necessary to convert a GPS height into a normal height. Normally, the conicoid fitting method (CFM) and the neural network method (NNM) are used for this purpose in China, but each has its own advantages and disadvantages. After studying these two methods, a new method (CF&NNM) is conceived. The structure of the back-propagation neural network and detailed algorithm of the CFM, NNM, and CF&NNM are discussed. The procedure of the new method is introduced. A practical engineering example is used to study these three different methods. The results by the three methods are listed. It is demonstrated that the CF&NNM could produce better results than either the CFM or the NNM in deriving the normal height from the GPS height. The theory of the CF&NNM is analyzed.

The commonly adopted remove-compute-restore (RCR) technique for regional gravimetric geoid determination uses the maximum degree of a combined global geopotential model and regional gravity data via the spherical Stokes integral. The University of New Brunswick’s (UNB) technique involves the use of a deterministically modified integration kernel, a degree-20 satellite-only reference field, integration of high-frequency terrestrial gravity anomalies over a spherical cap of 6° radius about each computation point, and a separate computation of the truncation bias used Degrees 21–120 of a combined global geopotential model. Both approaches are tested over Australia and the resulting geoid models compared with a nationwide dataset of 1,013 Global Positioning System (GPS)-leveled points, and with the most recent Australian geoid model, AUSGeoid98. A subsequent experiment considers the commission errors in the reference field used by applying a Wiener-type filter based on the global degree- and error-degree variances of the EGM96 combined and EGM96S satellite-only global geopotential models. The theoretical basis of this adapted approach will be presented, together with comparisons of the resulting geoid solution with the 1,013 GPS-leveling data, UNB, RCR, and AUSGeoid98 solutions.