Road detection statistics for automated quality control of GIS data

We examine the use of road detection in VHR satellite images to automate the process of quality assessment of digital road network data. An important aspect is the emphasis on accuracy and reliability of the system. Although road detection has been studied for more than a decade, it is often difficult to assess what performance can be expected over datasets other than the images published. We propose a system to train a road detector based on image examples of typical roads. The system calculates the optimal detection parameters and estimates the performance over the dataset in terms of detection rate and degree of fragmentation. The methodology relies on error propagation and image statistics, and is generic in nature. By showing image examples, variations due to shadow, activity on the road, weather conditions etc. can be taken into account when estimating the expected performance.