Abstract: This paper proposed an approach for quantifying daily exposure of urban residents to eye-level greenery. 280,000 street view images in Shanghai central area are collected for greenery analyses via machine learning. The integration of the street greenery with street accessibility helps to provide detailed guidance for better spatial quality on streets and efficient urban greenery planning. The comparison between this new index and the traditional urban green cover shows that the latter one might not accurately reflect accessed greenery for citizens. This study helps to achieve the co-present of large-scale but also high-resolution analysis. Moreover, it makes a step forward for a more human-centered planning policy.