Mapping suburban bicycle lanes using street scene images and deep learning
This work addresses the need for accurate bicycle lane mapping to improve cyclist safety and infrastructure planning, though it is incremental as it applies existing deep learning techniques to a specific domain.
The paper tackled the problem of incomplete and outdated bicycle lane maps by developing a method using street scene images and deep learning to detect and map bicycle lane symbols, successfully identifying unrecorded lanes in a Melbourne suburb.
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local authorities responsible for portions of the infrastructure, official maps and datasets of bicycle lanes may be out-of-date and incomplete.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.