Detecting Change in Road Networks Using Continuous Relaxation Labeling

In this paper we examine a system based on computer vision for automated detection of change and anomalies in GIS road networks using very high resolution satellite images. The system consists out of a low-level feature detection process, which extracts road junctions, and a high-level matching process, which uses graph matching to find correspondences between the detected image information and the road vector data. The matching process is based on continuous relaxation labelling. It is driven by spatial relations between the objects and takes into account different errors that can occur. The result is an object-to-object mapping between image and vector dataset. The mapping result can be used to calculate a rubbersheeting transformation which is able to compensate for local distortions. A measure of change is defined based on the number of null assignments. We show how combined with a condition to characterize acceptable errors this measure is useful and reliable to characterize inconsistencies between image and vector data.