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.

How to use Google street view for a time-lapse data collection methodology: potential uses for retailing

Finding the optimal location is a relevant strategic decision for retailers. The classic theories of retail location offer complementary perspectives, and later models include new variables, although they present methodological problems, these methodologies are static in time. Google Street View (GSV) allows extending the analysis of predictive models to different fields by a time-lapse collection data offering new opportunities to research and providing dynamic information. The development of a customized methodology, incorporating the time-lapse technique for practical applications, is the main contribution of this research, since there is almost no research on this topic.

Google Street View as a Teaching Tool for Assessing Earthquake Building Damage

Damage to infrastructure is one of the most visible impacts that earthquakes have on our daily lives. For this reason, introductory undergraduate courses in seismology and related fields often cover earthquake damage in relation to seismic hazard and risk. This topic is commonly introduced by viewing examples of damage, either through static images or videos. Subsequent coursework frequently involves excursions to the field for in-person site inspections and relating the damage to seismic intensity and other parameters of strong ground motion. These field visits provide students with valuable opportunities to apply classroom knowledge to real-world settings. At the time of writing, the recent COVID-19 pandemic has forced many university classes to cancel field visits, depriving students of these experiences. In this EduQuakes article, I present a lesson plan that attempts to simulate a field visit for assessing earthquake damage in an online setting using the interactive online resource Google Street View to view an area before and after an earthquake. This format facilitates active and exploratory learning and encourages students to build the necessary skills required for further studies in more advanced geoscience courses. I include the lesson plan and a compilation of relevant resources in the supplemental material to this article.