Who gets to use the street? Evaluate the utilization and inclusiveness using crowdsourced videos and vision-language models

• The study proposes a framework for evaluating public street spaces. • We collect data from riders to build the largest known street dataset in Beijing. • We train a vision-language model to identify demographic groups and behaviors. • The study reveals uneven pedestrian patterns across age groups and street segments. Equitable access to public street spaces (PSS) represents a fundamental prerequisite for sustainable urban development, yet systematic evaluation frameworks remain inadequately developed. Existing studies suffer from demographic homogenization assumptions, and insufficient data resolution. The deficiency undermines the effective utilization and full inclusiveness of PSS, ultimately constraining street vitality, social cohesion, and progress toward sustainable development goals. We introduce a crowdsourced data collection method based on food delivery riders, significantly enhancing the detail and coverage of the dataset. Therefore, we collect over 11,000 hours of video and construct the “EgoCity Dataset”. Subsequently, we develop a powerful vision-language model (VLM) capable of identifying demographic groups and behaviors (F1-score: 0.9583), addressing critical gaps in existing computer vision applications for urban analysis. Finally, we propose a framework for evaluating PSS, which quantifies the actual level of urban vitality and spatial equity through two key indicators: utilization and inclusiveness. The main findings are: (1) At the individual level, pedestrian distribution shows significant demographic disparities, with adults dominating both volume and activity diversity. (2) At the street level, there is a spatial mismatch between pedestrian flow and residential population. (3) At the regional level, spatial advantages and resources are concentrated in select areas at the expense of broader equity. Our study proposes a low-cost data collection method, and is the first to integrate crowdsourced street video data with vision-language models for pedestrian group identification, as well as to introduce a novel framework for evaluating utilization and inclusiveness in public street spaces. This study offers practical tools for urban planning and advances the sustainable development goals.