Improving positional accuracy and preserving topology through spatial data fusion

The spatial information industry is currently moving from an era of digital dataset production to one of dataset maintenance. This is being facilitated by the widespread availability of technologies such as Global Navigation Satellite Systems (GNSS) and high resolution imagery, which enable the rapid collection of large amounts of data with high positional accuracy. Typically, these new data are being integrated with existing datasets that possess different quality characteristics. As this trend continues, new spatial data quality concerns are emerging, in particular regarding the spatial variability of quality. Rigorous software solutions are needed that can handle the integration of data of different accuracies. This is already occurring for land parcel mapping, where least squares adjustment techniques have been developed to integrate high accuracy subdivision data with the existing cadastral database and its inherent geometry. A best fit between the two datasets is determined, together with well-defined local positional accuracy parameters, resulting in incremental improvement to the overall positional accuracy of the cadastral database. This research aims to extend such integration methodology to the fusion of more complex features within vertical vector datasets, whilst maintaining the relevant topological relationships between them. Vertical datasets cover the same regional extent and often include duplicate representations of the same features. There may also be rules governing the spatial relationships between features. It is proposed that the development of rigorous integration algorithms that allow for the inclusion of geometric and topological constraints will enable the successful fusion of vertical spatial datasets. Features will be optimally positioned and well-defined positional accuracy parameters will be generated that, in turn, may be reported as spatial variation in the quality of the resultant dataset. This will ultimately result in methods that can be used to improve existing spatial datasets, hence reducing the observed discrepancies as new, higher accuracy data are introduced.