We present a method for efficient and reliable geo-positioning of images. It relies on image-based matching of the query images onto a trellis of existing images that provides accurate 5-DOF calibration (camera position and orientation without scale). As such it can handle any image input, including old historical images, matched against a whole city. At that scale, care needs to be taken with the size of the database. We deviate from previous work by using 3600 panoramas to simultaneously reduce the database size and increase the coverage. To reduce the likelihood of false matches, we restrict the range of angles for matched features. Furthermore, we enhance the RANSAC procedure to include two phases. The second phase includes guided feature matching to increase the likelihood of positive matches. Hence, we devise a matching confidence score that separates between true and false matches. We demonstrate the algorithm on a large scale database covering a whole city and show its uses to vision-based augmented reality system.