A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning

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.

Quantifying the Characteristics of the Local Urban Environment through Geotagged Flickr Photographs and Image Recognition

“…Naik and his colleagues used an image segmentation approach and support vector regression to monitor neighbourhood changes and correlate socioeconomic characteristics to uncover predictors for the improvement of physical appearance [10]. More recent research developed a deep CNN model, a hierarchical urban forest index, to quantify the amount of vegetation visible based on street-level imagery [2].…”

“…Traditional approaches to understanding the urban environment have relied on survey data. These approaches can be used to characterise urban morphology, but they can generate gaps in data collection and data quality that are costly and problematic [2]. Although recently emerging street-level imagery data can overcome these gaps, these data are mostly from Google's own street view fleets, which rarely capture human perceptions of the urban environment.…”

Remote Sensing of Urban Forests

“…With the increasing availability of big data (e.g., multi-source satellite images) and computational power (e.g., cloud-computing), a new range of studies can take advantage of available technologies to enhance remote sensing capabilities at the urban scale. For example, Stubbings et al [11] developed a method based on semantic segmentation algorithms (Pyramid Scene Parsing Network) and a hierarchical multilevel model to retrieve a measure of the quantity of visible vegetation from pedestrians' point of view, namely the Urban Street Tree Vegetation Index. The method, which used more than 200,000 street-level images classified by means of Deep Convolution Neural Networks, represents a valuable addition to traditional remote sensing sources (e.g., aerial or satellite data).…”

“…Given the increasing availability of satellite images from different sensors, the spread of LiDAR data and growing potential of cloud-based services (i.e., Google Earth Engine or Amazon Web Services), there is a need for innovative research focusing on advanced remote sensing applications for monitoring and assessing urban forest areas and associated ESS. This Special Issue includes research studies focusing on the temporal dynamics of urban forests [9,10] and their distribution in space through the application of advanced semantic segmentation techniques [11] and in relationship with green space accessibility [12], the implementation of laser scanner for improving allometry-based forest biometrics [13], and a review investigating the state of the art of remote sensing in urban forestry [14] (see Table 1).…”

Urban Function as a New Perspective for Adaptive Street Quality Assessment

“…Street networks are considered to be one significant component of urban structures and serve different urban functions for sustainable development, including commercial, traffic, industry and landscape-based functions [1][2][3]. Assessing the quality of each street is important for managing natural and public resources, organizing urban morphologies and improving city vitality [4][5][6].…”