Abstract
We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two steps. First, areas of vegetation are detected within street-level imagery using a state-of-the-art deep neural network model. Second, information is combined from several images to derive an aggregated indicator at the area level using a hierarchical multilevel model. The comparative performance of our proposed approach is demonstrated against a widely used image segmentation technique based on a pre-labelled dataset. The approach is deployed to a real-world scenario for the city of Cardiff, Wales, using Google Street View imagery. Based on more than 200,000 street-level images, an urban tree street-level indicator is derived to measure the spatial distribution of tree cover, accounting for the presence of obstructing objects present in images at the Lower Layer Super Output Area (LSOA) level, corresponding to the most commonly used administrative areas for policy-making in the United Kingdom. The results show a high degree of correspondence between our tree street-level score and aerial tree cover estimates. They also evidence more accurate estimates at a pedestrian perspective from our tree score by more appropriately capturing tree cover in areas with large burial, woodland, formal open and informal open spaces where shallow trees are abundant, in high density residential areas with backyard trees, and along street networks with high density of high trees. The proposed approach is scalable and automatable. It can be applied to cities across the world and provides robust estimates of urban trees to advance our understanding of the link between mental health, well-being, green space and air pollution.
Highlights
Urban trees provide key social, environmental and economic benefits
This section first discusses the results of a comparative assessment between our proposed Pyramid Scene Parsing Network (PSPNet) approach and the pixel-based methods described in the previous section
These measures are commonly used to assess the accuracy of binary classification outcomes, and comprise: True Positive (TP), False Positive (FP); True Negative (TN) and False Negative (FN)
Summary
Urban trees provide key social, environmental and economic benefits. Urban green space, including parks, green roofs, community gardens and street trees, provide critical ecosystem services. These spaces promote physical activity, boost psychological well-being, filter air, remove pollution, attenuate noise, cool temperatures, mitigate climate change and improve urban public health [2,3]. Urban trees play a major role in removing environmental air pollution and improving human health. By providing shade and cooling, urban trees moderate temperatures, helping to reduce the risk of heat-related illnesses by an estimated 4–6 degrees [5,6]. Urban green space has key health benefits. Ensuring widespread access to urban green space is key to rendering these environmental and health benefits
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