As an important part of the urban ecosystem, urban trees provide various benefits to urban residents. It is therefore important to examine the spatial distribution and the temporal change in urban tree canopies. Different from traditional overhead view remote sensing-based methods, street-level images, which present the most common view that people have of greenery, provide a more human-centric way to quantify street tree canopies. This study mapped and analyzed the spatial distribution and temporal change in the green view index, which represents the visibility of tree canopies along streets in New York City during the last 10 years using historical Google Street View images. Deep learning and computer vision algorithms were used to derive the quantitative information of street tree canopies from street-level images and map the spatial distribution of the green view index. This study further investigated the potential disparities in terms of green view index across different racial/ethnic groups by comparing with census data. Results show that non-Hispanic Whites tend to live in neighborhoods with higher green view index and Hispanics tend to live in neighborhoods with lower green view index. The green view index values in New York City have increased slightly in the last 10 years, and the change of green view index has no significant correlation with resident’s ethnic/racial status. This study proves the usability of historical Google Street View images for monitoring the temporal change of urban street tree canopy changes at large scale, and it also provides insights and a valuable reference for urban greening programs.
Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments