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

Keywords: street view image, land cover, sidewalk, width, wheelchair users
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 02:00 PM (Eastern Time (US & Canada)) - 3/1/2022 03:20 PM (Eastern Time (US & Canada))
Room: Virtual 20

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 control; 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 widths for wheelchair users. Sidewalk feature slopes were also extracted from the metadata of street view images. Using the Washington D.C., U.S. study area, our method extracted a sidewalk network of 2,500 km in length with the precision of 0.8662 and recall of 0.8525. For sidewalks with widths between 1 – 2 m, the mean width error was 0.24 m. The slope mean error was 0.676°. 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.