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In this paper an approach to the automatic quality assessment of existing geo-spatial data is presented. The necessary reference information is derived automatically from up-to-date digital remotely sensed images using image analysis methods. The focus is on the quality assessment of roads as these are among the most frequently changing objects in the landscape. In contrast to existing approaches for quality control of road data, the data to be assessed and the objects extracted from the images are modelled and processed together. A geometric-topologic relationship model for the roads and their surroundings is defined. Context objects such as rows of trees support the quality assessment of road vector data as they may explain gaps in road extraction. The extraction and explicit incorporation of these objects in the assessment of a given road database give stronger support for or against its correctness. During the assessment existing relations between road objects from the database and extracted objects are compared to the modelled relations. The certainty measures of the objects are integrated into this comparison. Normally, more than one extracted object gives evidence for a road database object; therefore, a reasoning algorithm which combines evidence given by the extracted objects is used. If the majority of the total evidence argues for the database object and if a certain amount of this database object is covered by extracted objects, the database object is assumed to be correct, i.e. it is accepted, otherwise it is rejected. The procedure is embedded into a two-stage graph-based approach which exploits the connectivity of roads and results in a reduction of false alarms. The algorithms may be incorporated into a semi-automatic environment, where a human operator only checks those objects that have been rejected. The experimental results confirm the importance of the employed advanced statistical modelling. The overall approach can reliably assess the roads from the given database, using road and context objects which have been automatically extracted from remotely sensed imagery. Sensitivity analysis shows that in most cases the chosen two-stage graph-approach reduces the number of false decisions. Approximately 66% of the road objects have been accepted by the developed approach in an extended test area, 1% has been accepted though incorrect. Those false decisions are mainly related to the lack of modelling road junction areas.