Converting street view images to land cover maps for metric mapping: A case study on sidewalk network extraction for the wheelchair users

Abstract

Street view images are now widely used in web map services, providing on-site photos of street scenes for users to explore without physically being in the field. These photos record detailed visual information of the street environment with geospatial controls; therefore, they can be used for metric mapping purposes. In this study, we present a method to convert street view images to measurable land cover maps using their associated depthmap data. The proposed method can autonomously extract and measure land cover objects over large areas covered by a mosaic of street view images. In the case study, we demonstrated the use of land cover maps derived from Google Street View images to extract sidewalk features and to measure sidewalk clear widths for wheelchair users. Sidewalk feature slopes were also extracted from the metadata of street view images. Using the Washington D.C., U.S. as the study area, our method extracted a sidewalk network of 2561 km in length with the precision of 0.8662 and recall of 0.8525. The width mean error of extracted sidewalks wide between 1 and 2 m is 0.24 m, and the slope mean error is 0.638°. In Washington D.C., most sidewalks meet the minimum width requirement (0.9 m), but 20% of them have slopes that exceed the maximum allowance (1:20 or about 2.9°). These results demonstrate the converted land cover maps from street view images can be used for metric mapping purposes. The extracted sidewalk network can serve as a valuable inventory for urban planners to promote equitable walkability for mobility disabled users. And if widely available, mobility-impaired users could consult them prior to planning a route.