The sidewalk is an indispensable infrastructure for pedestrians, especially wheelchair users. Wheelchair users rely on quality sidewalks to facilitate safe and uninterrupted trips in their everyday lives. Transportation agencies are required to evaluate regulatory compliance with the Americans with Disabilities Act (ADA) and are responsible for the timely maintenance of inadequate sidewalks. However, these timely evaluation and maintenance activities are usually lacking because of the labor-intensive and cost-prohibitive data collection process in the current practice. There is an urgent need for an efficient and reliable method for assessment of sidewalk compliance with the ADA. This paper aims to address such a need by proposing an automated sidewalk assessment method using three-dimensional mobile light detection and ranging (lidar) and image processing. The presence of sidewalks and curb ramps is first extracted by use of a video log image and a lidar point cloud. Then, the corresponding key features regulated by the ADA, including the sidewalk width, cross slope, and grade and the curb ramp slope, are automatically measured. By comparison with the manual ground truth from a field survey, the experimental tests conducted on the Georgia Institute of Technology campus at Atlanta, Georgia, showed accurate measurement results for the key features of the sidewalk and curb ramps. A case study was then conducted to demonstrate that the proposed method could provide transportation agencies a convenient and cost-effective means of assessment of compliance with the ADA by integration of accurately extracted sidewalk location and measurement information.