Automated Compliance Assessment for Sidewalks Using Machine Learning

State and local governments are required by federal and state laws to provide and maintain accessibility for people with disabilities on their sidewalks and pedestrian facilities. They need to conduct and frequently update self-evaluation to assess the compliance of their sidewalks and pedestrian facilities with accessibility requirements and identify any barriers that limit or deny access for people with disabilities to public services, programs, or activities. This paper presents a framework that consists of two modules that are designed to (1) capture and document sidewalk conditions, and (2) assess the compliance of these sidewalks with accessibility requirements. The framework utilizes machine learning, photogrammetry, and point cloud analysis to automate the extraction of sidewalk dimensions and conditions from captured images and assesses their compliance with accessibility requirements. The framework is designed to support state and local government officials in automating and expediting the assessment of the existing conditions of their sidewalk networks.