In Flanders (Belgium) the large scale geographic reference database called GRB, takes care of the layout, exchange and management of large scale geographic information with respect to, amongst others, roads, buildings and parcels. As Flanders is extremely urbanized (average population density of about 450 inhabitants per square kilometer), the large scale maps need to be highly accurate. Currently, accuracies at the centimeter level are guaranteed due to topographic field measurements aided by standard photogrammetry based on analogue aerial photographs. In order to speed up the GRB production and to ensure large scale map products at the long term, it is essential to automate the labour-intensive, but highly accurate production process. As large format digital aerial cameras become readily available and are continuously improving with regard to their geometric and radiometric accuracy, very high resolution digital images can serve as a basis for large scale mapping purposes. A crucial step in treating very high resolution digital data in such an automatic workflow is image segmentation. And even more essential is the accuracy of the segmentation result. Although a great variety of segmentation algorithms have been implemented in commercial software packages, few efforts have been spent on their quantitative evaluation. High accuracy segmentation results are prerequisite in order for image segmentation of very high resolution data to qualify for maintaining and updating the Flemish GRB. Furthermore the incorporation of image segmentation in a regional operational workflow requires bypassing the highly subjective and labour intensive stage of trial and error to obtain suitable parameter settings. Based on DMC images (8 cm ground resolution), this paper aims at developing a methodology for a supervised, objective evaluation of segmentation quality, based on quantitative similarity measures.