  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.
