In this project, we investigate the potential for new sensing modalities to help drive progress on streetscahep accessibility by reproducing a geographic section of a major data collection exercise - the Broadway Curb Cut Survey - using updated technologies. We compare three data collection methods: (i) a smartphone-based field survey with user-generated photos and curb cut locations; (ii) the mass-market 3D imaging device 'Structure Sensor'; and (iii) a 360-degree LiDAR scanner. Post-processing of the point cloud data on Broadway curb cuts is conducted, physical measurements are extracted, and a web-map is constructed to make data on accessibility problems easily accessible to decision-makers. Challenges identified include measurement of slope angles and accurate geo-location of the curbs. Having refined the methods through field testing, our work demonstrates the feasibility of 3D imaging to acquire useful information on physical properties of city streets - including slope width and steepness, presence of bumps and drops, and potholing. Such sensing methods may also help alleviate behavioral constraints to crowd-sourcing of accessibility data. Based on stakeholder requirements and our evaluation of the data collection methods, we propose the creation of a smartphone application that would combine survey methods with 3D imagery.