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.
Access the data files
Due to the size (~550 GB) and complexity of the data, you must access it via Globus: https://app.globus.org/file-manager?origin_id=c43d41ac-d286-4ac4-9318-3d65f3d9b855&origin_path=%2Fq1byv-qc065-streetaware%2F. The README file explains the contents further.

搜集汇总

背景与挑战

背景概述

StreetAware是一个高分辨率、多模态同步的城市场景数据集，包含音频、视频和LiDAR数据，采集自纽约布鲁克林的三个交叉口，总时长约8小时。该数据集旨在支持计算机视觉和城市设计研究，通过自定义REIP传感器实现精确数据同步，数据量约550 GB，需通过Globus平台访问。