Estimation of Mean Radiant Temperature in Urban Canyons Using Google Street View: A Case Study on Seoul

Extreme heat exposure has severe negative impacts on humans, and the issue is exacerbated by climate change. Estimating spatial heat stress such as mean radiant temperature (MRT) is currently difficult to apply at city scale. This study constructed a method for estimating the MRT of street canyons using Google Street View (GSV) images and investigated its large-scale spatial patterns at street level. We used image segmentation using deep learning to calculate the view factor (VF) and project panorama into fisheye images. We calculated sun paths to estimate MRT using panorama images from Google Street View. This paper shows that regression analysis can be used to validate between estimated short-wave, long-wave radiation and the measurement data at seven field measurements in the clear-sky (0.97 and 0.77, respectively). Additionally, we compared the calculated MRT and land surface temperature (LST) from Landsat 8 on a city scale. As a result of investigating spatial patterns of MRT in Seoul, South Korea, we found that a high MRT of street canyons (>59.4 °C) is mainly distributed in open space areas and compact low-rise density buildings where the sky view factor is 0.6–1.0 and the building view factor (BVF) is 0.35–0.5, or west-east oriented street canyons with an SVF of 0.3–0.55. However, high-density buildings (BVF: 0.4–0.6) or high-density tree areas (Tree View Factor, TVF: 0.6–0.99) showed low MRT (<47.6). The mapped MRT results had a similar spatial distribution to the LST; however, the MRT was lower than the LST in low tree density or low-rise high-density building areas. The method proposed in this study is suitable for a complex urban environment consisting of buildings, trees, and streets. This will help decision makers understand spatial patterns of heat stress at the street level.

Assessing soil sealing and ecosystem services of urban front yards using Google Street View: A case study in Bloemhof district Rotterdam, the Netherlands

Private gardens play an important role as urban green space in cities and can improve the microclimate and address the impacts of climate change. Paving over front yards, soil sealing, reduces the environmental benefit of front yards. Residential private front yards comprise a considerable portion of land and green space in the suburbs of cities. Currently there is no method available to determine sealed soil percentages of private front yards. This study took place in the Bloemhof suburban district in Rotterdam. Four streets were selected and a total of 123 houses with 105 private front yards were assessed. Five sealed soil reference categories were defined and Google Street View (GSV) images were used to assess the front yards. This study found that the aggregated sealed soil percentages of the private front yards in the four selected streets are very high: 69%, 78%, 96%, and 97%. These front yards have a significant greening potential. The new insight in this study is that the use of Google Street View images for categorisation of front yards leads to values for individual front yard that can be used for comparison and for establishing sealed soil values per street.

Proposta de metodologias de levantamento remoto do espaço público destinado a vagas de estacionamento no sistema viário

O espaço viário pode assumir o papel de acolher encontros sociais e trazer mais vivacidade às cidades, porém muitas vezes esses espaços são ocupados por estacionamentos de automóveis, sendo necessária a quantificação de vagas para se dimensionar e adequar os espaços utilizados, inclusive para outros usos. Este trabalho objetiva propor e comparar duas metodologias para levantamento remoto de vagas de estacionamento no meio fio urbano. A primeira metodologia obtém o número de vagas a partir da medição gráfica do comprimento de meio fio disponível para estacionar dividido pelo comprimento de uma vaga padrão. A segunda metodologia é baseada na identificação e marcação da posição de veículos parados nas imagens disponibilizadas no Google Street View. Para a aplicação das metodologias foi selecionada a área da ZEU Butantã, área de adensamento urbano localizada no município de São Paulo, onde o transporte ativo e público coletivo são prioritários. Os resultados das aplicações metodológicas na escala da ZEU mostraram similaridades, porém na escala da via, apresentaram uma diferença de até três vagas. Foi observado que 5,88% da área do viário da ZEU Butantã está sendo destinada ao estacionamento de automóveis, representando 1,37% da área total, o que, ainda que parcialmente, poderia ser revertido para implantação de ciclovias ou alargamento de passeios públicos, necessários na região.

Evaluating the Street Greening with the Multiview Data Fusion

Street greening, an indispensable element of urban green spaces, has played an important role in beautifying the environment, alleviating the urban heat island effect, and improving residents’ comfort. Vegetation coverage is a common index used for measuring street greening. However, there are some shortcomings in the traditional evaluation methods of vegetation coverage. Part of the vegetation coverage cannot be determined from a two-dimensional perspective, such as shrubs and green walls. In this paper, the Sentinel-2 image was used to extract the street fractional vegetation cover (SFVC) and the Baidu street view panoramas were used to extract the green view index (GVI). To overcome the lack of a single perspective from the street vegetation coverage evaluation, the above two indices were merged to construct a comprehensive street greening evaluation index (CSGEI). The research area is the Longhua District of Shenzhen city in Southern China. All three indices were divided into five classes using natural breakpoint methods based on previous research experience. The results showed that Baidu street view panoramas could effectively identify shrubs and green walls that were deficient in the Sentinel-2 image. The GVI is a supplement to the street vegetation coverage. The SFVC and GVI were divided into five classes, from L1 to L5 implying a gradual increase in the percentage of the vegetated area. The result has shown that the SFVC was in the L1, accounting for 53.68%. After index merging, the process of accounting for the L1 decreased to 31.29%. The multiperspective integrated CSGEI could comprehensively measure the distribution information of street greening and guide the planning and management of urban green landscapes.

Heterogeneity Study of the Visual Features Based on Geographically Weighted Principal Components Analysis Applied to an Urban Community

Communities in urban space are the most basic living units. Community visual features directly reflect the local living quality and influence the perception of residents and visitors. The evaluation of the community visual features is of great significance to the space design under the guidance of urban landscape recognition and urban space perception. Based on the street view image data, this paper analyzes the composition of visual features in the community space scale by using the geographically weighted principal components analysis. GWPCA can not only reflect the global characteristics, but also analyze the local components, thus describing the visual features of the community in a comprehensive manner. The results show that: (1) community visual features have significant spatial heterogeneity at different statistical scales, and the spatial heterogeneity of community visual features can provide a basis for urban landscape planning and design; (2) the combination mode of dominant visual elements can reflect different community landscapes. The analysis of this paper further illustrates the effectiveness and application prospect of street view images in identifying the landscape composition mode of urban space from the medium-micro perspective. This conclusion is helpful for planners to learn the dominant visual features of the community through street view images, and, further, use the classification of elements of street view images to guide the planning and design of cityscape.