OASIS: Automated Assessment of Urban Pedestrian Paths at Scale
Yuxiang Zhang, Suresh Devalapalli, Sachin Mehta, Anat Caspi

TL;DR
OASIS is an open-source system that automates sidewalk inspection and mapping using neural networks and mobile devices, improving efficiency and data quality for urban planning and accessibility monitoring.
Contribution
We introduce OASIS, a novel automated sidewalk assessment system that integrates AI, hardware, and GIS to enhance urban accessibility data collection.
Findings
High precision and recall in sidewalk path mapping (0.94, 0.98)
Increased efficiency of surveyor teams in the field
System is designed for easy integration with government workflows
Abstract
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…
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Taxonomy
TopicsTraffic and Road Safety · Automated Road and Building Extraction · Traffic Prediction and Management Techniques
MethodsOASIS
