2023/03/04 by Yuxiang Zhang, Suresh Devalapalli, Zhang, Yuxiang +5 · 1 citation
Engineering · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Traffic Prediction and Management Techniques #Traffic and Road Safety
paper · pdf · doi:10.48550/arxiv.2303.02287
openalex publication_date 2023/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The inspection of the Public Right of Way (PROW) for accessibility barriers is necessary for monitoring and maintaining the built environment for communities' walkability, rollability, safety, active transportation, and sustainability. However, an inspection of the PROW, by surveyors or crowds, is laborious, inconsistent, costly, and unscalable. The core of smart city developments involves the application of information technologies toward municipal assets assessment and management. Sidewalks, in comparison to automobile roads, have not been regularly integrated into information systems to optimize or inform civic services. We develop an Open Automated Sidewalks Inspection System (OASIS), a free and open-source automated mapping system, to extract sidewalk network data using mobile physical devices. OASIS leverages advances in neural networks, image sensing, location-based methods, and compact hardware to perform sidewalk segmentation and mapping along with the identification of barriers to generate a GIS pedestrian transportation layer that is available for routing as well as analytic and operational reports. We describe a prototype system trained and tested with imagery collected in real-world settings, alongside human surveyors who are part of the local transit pathway review team. Pilots show promising precision and recall for path mapping (0.94, 0.98 respectively). Moreover, surveyor teams' functional efficiency increased in the field. By design, OASIS takes adoption aspects into consideration to ensure the system could be easily integrated with governmental pathway review teams' workflows, and that the outcome data would be interoperable with public data commons.