  On-road bicycle lanes improve safety for cyclists, and encourage
participation in cycling for active transport and recreation. With many local
authorities responsible for portions of the infrastructure, official maps and
datasets of bicycle lanes may be out-of-date and incomplete. Even
"crowdsourced" databases may have significant gaps, especially outside popular
metropolitan areas. This thesis presents a method to create a map of bicycle
lanes in a survey area by taking sample street scene images from each road, and
then applying a deep learning model that has been trained to recognise bicycle
lane symbols. The list of coordinates where bicycle lane markings are detected
is then correlated to geospatial data about the road network to record bicycle
lane routes. The method was applied to successfully build a map for a survey
area in the outer suburbs of Melbourne. It was able to identify bicycle lanes
not previously recorded in the official state government dataset,
OpenStreetMap, or the "biking" layer of Google Maps.
