Context and objectives
Tele Atlas is the market leader in the production of digital road maps in a wide variety of applications all over Europe and the USA. The company is faced with the problem of upgrading its current databases to a higher accuracy and ensuring the quality of the information. Current techniques can’t support this in a cost-effective way due to the necessary manpower. Automated detection of change and anomalies in the existing databases using very high resolution (VHR) satellite images can form an essential tool to support quality control and maintenance of spatial information.
Quality of geospatial data is still an evolving concept. At first glance, it seems a simple concept but devising measures which in a meaningful way quantize different discrepancies that can occur in the data is not straightforward. Two questions will be dealt with within the project:
Expected scientific results
In the first part, two ridge detectors are introduced. The performance of the road extraction is characterized in terms of the detection rate and mean segment length. The results show that there is a good correspondence between the predicted and the measured detector performance. This enables us to set up the detector parameter set in such way an optimal extraction of the road features can be expected. As a result, a prototype has been created to extract road features out of various image sources without the need of an image processing expert. Experiments have been conducted on a set of images of typical roads in an IKONOS satellite image to verify the validity of the derived expressions.
In the second part of the research, a thorough analysis of the literature has been conducted concerning quality assessment of linear vector databases. We set up a framework for applying the selected procedure, i.e. Buffer Overlay Statistics (BOS), on both vector- and raster-layers. Using this methodology we did an assessment of the quality of the existing TeleAtlas-database. We can conclude that the average displacement of the TA-data in the considered test zone is 1,8m.
To conclude, we set up a generic framework to automatically assess the quality of a road network database based on very high resolution images. Two approaches were tested starting from reliable and unreliable georegistration of the imagery. The first approach used the developed Image-based Buffer Overlay concept, the second approach error tolerant graph matching.
|Project leader(s):||UGent - Image Processing and Interpretation Research Group (IPI)|