StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset

Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors at areas of high activity in the urban environment, such as street intersections, are thus a valuable resource for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. We present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling approximately 8 unique hours. The data is collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that are designed with the ability to accurately synchronize multiple video and audio inputs.