Classification of sidewalks in street view images

Mapping sidewalks in urban environments is key in the creation of pedestrian-friendly, sustainable cities. Currently, urban planners are hindered by a lack of information available in a format suitable for the large-scale analysis of sidewalk design. To demonstrate the impact that information technology could have in this area, we leverage techniques from machine learning and computer vision to gather information about the presence and quality of sidewalks in map images. In particular, we identify sidewalk segments in street view images using a random forest classifier, utilizing a set of local and global features that include geometric context, presence of lanes, pixel color, and location. Our results illustrate that this approach is effective in classifying sidewalk segments in a large set of street view images. This algorithm can be easily extended to other datasets, and can be automated to gather complete, fine-grained details about sidewalks for arbitrarily large urban environments.