  Navigation services utilized by autonomous vehicles or ordinary users require
the availability of detailed information about road-related objects and their
geolocations, especially at road intersections. However, these road
intersections are mainly represented as point elements without detailed
information, or are even not available in current versions of crowdsourced
mapping databases including OpenStreetMap(OSM). This study develops an approach
to automatically detect road objects and place them to right location from
street-level images. Our processing pipeline relies on two convolutional neural
networks: the first segments the images, while the second detects and
classifies the specific objects. Moreover, to locate the detected objects, we
establish an attributed topological binary tree(ATBT) based on urban grammar
for each image to depict the coherent relations of topologies, attributes and
semantics of the road objects. Then the ATBT is further matched with map
features on OSM to determine the right placed location. The proposed method has
been applied to a case study in Berlin, Germany. We validate the effectiveness
of our method on two object classes: traffic signs and traffic lights.
Experimental results demonstrate that the proposed approach provides
near-precise localization results in terms of completeness and positional
accuracy. Among many potential applications, the output may be combined with
other sources of data to guide autonomous vehicles
