Last years witnessed the growth of demand for road monitoring systems based on image or video analysis. These systems usually consist of a survey vehicle equipped with photo and video cameras, laser scanners and other instruments. Sensors mounted on the van collect different types of data while the vehicle goes along the road. Recorded video can be geographically referenced with the help of global positioning systems. Road monitoring systems require special software for data processing. This paper addresses the problem of video analysis automation, and particularly the pavement monitoring functionality of such mobile laboratories. We show that computer vision methods applied to this problem help to reduce amount of manual labour during data analysis. Our method transforms video collected by mobile laboratory into rectified geo-referenced images of road pavement surface, and allows mapping of lane marking and road pavement defects with minimum user interaction. In our work the mapping workflow consists of two stages: off-line and online stage. In order to reduce user effort during error correction we take advantage of hierarchical image segmentation, which helps to delete false detections or mark missing objects with just a few clicks. Through continuous training of detection algorithm with the help of operator input error rate of automatic detection decreases; thus minimal input is required for accurate mapping. Experiments on real-world road data show effectiveness of our approach.