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

Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea

Understanding the relationship between land use/land cover (LULC) and land surface temperature (LST) has long been an area of interest in urban and environmental study fields. To examine this, existing studies have utilized both white-box and black-box approaches, including regression, decision tree, and artificial intelligence models. To overcome the limitations of previous models, this study adopted the explainable artificial intelligence (XAI) approach in examining the relationships between LULC and LST. By integrating the XGBoost and SHAP model, we developed the LST prediction model in Seoul and estimated the LST reduction effects after specific LULC changes. Results showed that the prediction accuracy of LST was maximized when landscape, topographic, and LULC features within a 150 m buffer radius were adopted as independent variables. Specifically, the existence of surrounding built-up and vegetation areas were found to be the most influencing factors in explaining LST. In this study, after the LULC changes from expressway to green areas, approximately 1.5 °C of decreasing LST was predicted. The findings of our study can be utilized for assessing and monitoring the thermal environmental impact of urban planning and projects. Also, this study can contribute to determining the priorities of different policy measures for improving the thermal environment.

Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review

Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale. The previous reviews have mainly focused on specific health findings and neighbourhood environments. There has not been a comprehensive review of this topic. In this paper, we systematically review the literature on the application of SVI in the built environment, following a formal innovation–decision framework. The main findings are as follows: (I) SVI remains an effective tool for automated research assessments. This offers a new research avenue to expand the built environment-measurement methods to include perceptions in addition to physical features. (II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. (III) The significant dilemmas concerning the adoption of this technology are related to image acquisition, the image quality, spatial and temporal distribution, and accuracy. (IV) This research provides a rapid assessment and provides researchers with guidance for the adoption and implementation of SVI. Data integration and management, proper image service provider selection, and spatial metrics measurements are the critical success factors. A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social spaces.

Evaluating the Annual Effect of the Sky View Factor on the Indoor Thermal Environment of Residential Buildings by Envi-met

The effect of the sky view factor (SVF) on outdoor thermal comfort has been extensively explored, while its impact on the indoor thermal environment is ignored. This research combined Envi-met and kriging models to explore the annual effect of the sky view factor on the indoor thermal environment. Different from previous studies, this study explored the effect of the sky view factor on indoor temperature rather than outdoor temperature, and from the perspective of a full year instead of a typical summer day. The analytical results reveal that an increase in the sky view factor raised the indoor air temperature every month. Although a low sky view factor was beneficial to the insulation of the built environment at night, it was proven that in Chenzhou city, the indoor air temperature was still higher in a built area with a high sky view factor than with a low sky view factor. In addition, the sky view factor was shown to have a nonlinear relationship with indoor thermal comfort throughout the year. When the sky view factor increased from 0.05 to 0.45, the indoor temperature increased by around 10 °C at 16:00 and increased by about 4 °C throughout the night for each month, and from the view of the annual cycle, the cooling demand duration increased by 1611.6 h (18.4%), and the heating demand duration decreased by 1192.3 h (13.61%).