Abstract for presentation at Spatial Sciences Institute International Biennial Conference

As the spatial information industry moves from an era of digital dataset production to one of dataset maintenance, new questions are arising. One of these is how best to integrate new data into existing databases. With the widespread availability of technologies such as Global Navigation Satellite Systems (GNSS) and high resolution imagery, large amounts of data with high positional accuracy can be rapidly acquired.
Discrepancies are becoming increasingly obvious between these higher accuracy data and databases that may possess different quality characteristics. Ideally, the new data could be used to upgrade the quality of existing datasets. This requires rigorous software solutions that can handle the integration in a way that accounts for the spatial variability of quality in both the input and resulting datasets. A case study is presented here, demonstrating the use of survey-accurate data to upgrade the cadastre in a North Western region of Victoria. Although not collected for this purpose, a sample of approximately 14000 GNSS points from a recent survey conducted within the Wimmera Mallee Pipeline Project was made available for the adjustment. The existing cadastral database for this rural region had been manually digitised; its positional accuracy being reported as 90% of well-defined points being within 25m of their true position. Using software that performed a transformation based on the method of least squares, the positional accuracy of this region of the cadastre was considerably improved. This report describes some of the challenges involved in performing the adjustment, together with recommendations for a methodology to extend such integration methods to the complete Victorian cadastre.