Mapping urban greenery in cities around the world using Google Street View

Xiaojiang Li, Bill Yang Cai, Carlo Ratti, Using Street-level Images and Deep Learning for Urban Landscape Analysis, Landscape Architecture Frontier, 2018.

Cai, B. Y., Li, X., Seiferling, I., & Ratti, C. (2018, July). Treepedia 2.0: applying deep learning for large-scale quantification of urban tree cover. In 2018 IEEE International Congress on Big Data (BigData Congress) (pp. 49-56). IEEE.

Fangying Gong, Zhaocheng Zeng, Fan Zhang, Xiaojiang Li, Edward Ng, Les Norford. (2018). Mapping sky, tree, and building view factors of street canyons in a high-density urban environment, Building and Environment. 134, 155-167.

PSPNet Convolutional neural network

Streetscape environment and human body mass index (BMI)

Streets are the basic unit for human activities in cities, it is important to understand how the streetscape environment can influence human health conditions. In this study, we investigated the influence of street greenery and walkability on body mass index in Cleveland, Ohio, USA. Different from the area level and overhead view greenery metrics, we used the green view index calculated from the Google Street View to represent the amount of street greenery. The Walk Score was used to indicate the walkability of neighborhoods also at the street level. Statistical analysis results show that the Walk Score has a more significant association with decreased BMI for males than females and the street greenery has a more significant association with decreased BMI for females than males in Cleveland, Ohio. The results of this study would provide a reference for designing gender-specific healthy cities.

Li, X., & Ghosh, D. (2018). Associations between Body Mass Index and Urban “Green” Streetscape in Cleveland, Ohio, USA. International journal of environmental research and public health, 15(10), 2186. (Link)

Streetscape and Human Walking activities

Having an active lifestyle is recognized to positively contribute to public health. Creating more walkable streets and neighborhoods is an important way to promote an active lifestyle for urban residents. It is therefore important to understand how the urban built environment can influence human walking activities. In this study, we investigated the interaction of human walking activities and physical characteristics of streetscapes in Boston. A large number of anonymous pedestrian trajectories collected from a smartphone application were used to estimate human walking activities. Publicly accessible Google Street View images were used to estimate the amount of the street greenery and enclosure of street canyons, both of which were used to indicate the physical characteristics of streetscapes.

Xiaojiang Li, Paolo Santi, ..., Carlo Ratti. Investigating the association between streetscapes and human walking activities using Google Street View and human trajectory data. Transactions in GIS, 2018.

Fig.5. The spatial distributions of independent variables (a) the Walk Score, (b) the Green View Index, (c) the enclosure of streetscapes enclosed by buildings, (d) the enclosure of streetscapes enclosed by the street tree canopies.

Mapping urban greenery in cities around the world using Google Street View

As a dimensionless parameter of urban geometry, the sky view factor (SVF) indicates how much sky is obstructed. The SVF also represents the ratio between radiation received by a planar ground and that from the entire hemisphere’s input radiation. The sky view factor (SVF) has been widely applied in forestry, urban climate, air pollution, and urban heat island studies. I have developed a method to calculate SVF using the publicly accessible and globally Google Street View data. It is possible to map SVF at large scale for any place with Google Street View service available, which seems impossible before.

Li. X., Ratti. C., Seiferling. I, 2017, Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View, Landscape and Urban Planning, 169, 81-91.

Mapping shade provision of street trees using Google Street View

Street trees provide shade and increase human thermal comfort during hot summer. This study investigated the spatial distribution of the shade provision of street trees in Boston, Massachusetts using Google Street View and building height model. The result shows that the street trees help to decrease the SVF by 24.61% in street canyons of Boston.

Xiaojiang Li, et al. Mapping the spatial distribution of shade provision of street trees in Boston using Google Street View panoramas, Urban Forestry and Urban Greening, (Accepted).

Li, X., Ratti, C., & Seiferling, I. (2018). Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View. Landscape and Urban Planning, 169, 81-91.

Urban Form analysis using GSV panoramas

City streets are a focal point of human activity in urban centers. Citizens interact with the urban environment through its streetscape and it is imperative to, not only map city streetscapes but quantify those interactions in terms of human well-being. Google Street View (GSV), which captures the profile view of streetscapes and, thus, shares equivalent viewing angles with those of the citizen. Based on machine learning and image analysis, GSV can be utilized as a high-quality data source for mapping street greenery and openness.

Li, X., Ratti, C., & Seiferling, I. (2017, July). Mapping Urban Landscapes Along Streets Using Google Street View. In International Cartographic Conference (pp. 341-356). Springer, Cham.

Li, X., & Ratti, C. (2019). Using Google Street View for Street-Level Urban Form Analysis, a Case Study in Cambridge, Massachusetts. In The Mathematics of Urban Morphology (pp. 457-470). Birkhäuser, Cham.

Figure. Modeling solar radiation using building height model

Human perceived safety and visibility of greenery

Urban green space provides a series of esthetic, environmental and psychological benefits to urban residents. However, the relationship between the visibility of green vegetation and perceived safety is still in debate. This research investigated whether green vegetation could help to increase the perceived safety based on a crowdsourced dataset: the Place Pulse 1.0 dataset. Place Pulse 1.0 dataset, which was generated from a large number of votes by online participants, includes geo-tagged Google Street View images and the corresponding perceived safety score for each image. Results show that the visibility of green vegetation plays an important role in increasing perceived safety in urban areas. For different land use types, the relationship between vegetation structures and perceived safety varies. In residential, urban public/institutional, commercial and open land areas, the visibility of vegetation higher than 2.5 m has significant positive correlations with perceived safety, but there exists no significant correlation between perceived safety and the percentage of green vegetation in transportation and industrial areas. The visibility of vegetation below 2.5 m has no significant relationship with the perceived safety in almost all land use types, except for multifamily residential land and urban public/institutional land.

Li, X., Zhang, C., & Li, W. (2015). Does the visibility of greenery increase perceived safety in urban areas? Evidence from the place pulse 1.0 dataset. ISPRS International Journal of Geo-Information, 4(3), 1166-1183.