One of the main tasks of any city government is to keep infrastructure such as roads, signs, accessibility ramps, and sewers in perpetual working order. The first step of maintenance is timely identification of a problem. However, many infrastructure defects are left undetected and unattended for long periods of time. The City of Austin possesses several fleets of vehicles, which work regularly around the city to inspect infrastructure. We will investigate how to leverage those vehicles with state-of-the-art computer vision, robotics, and data science techniques, to automate infrastructure inspection in such a way that is publicly acceptable, will reduce costs, and will increase effectiveness of city maintenance efforts.
Peter Stone (Department of Computer Science), Miriam Solis (School of Architecture), Bryan Thompson (City of Austin Public Works, Department Systems and Information Management), Marc Coudert (City of Austin Office of Sustainability), Harel Yedidsion (Department of Computer Science) and Xuesu Xiao (Department of Computer Science)