Thermal Comfort in Sight: Thermal Affordance and its Visual Assessment for Sustainable Streetscape Design

TL;DR

This work defines thermal affordance and develops the Visual Assessment of Thermal Affordance ($VATA$) framework to predict and map outdoor thermal comfort ($OTC$) potential from Street View Imagery ($SVI$). It fuses objective image features ($IF$) with subjective visual-perceptual indicators ($VPI$) via a two-stage multi-task neural network learning ($MTNNL$) approach, complemented by a two-stage elastic net regression ($ENRM$) for interpretable inference. Validation against field OTC data from Singapore shows that $VATA$ predictions align with observed comfort and provide superior explanatory power when combined with physiological indicators, enabling high-resolution geospatial mapping of thermal affordance ($VATA$ maps). The framework is extensible to other cities and supports data-driven, design-guided sustainable streetscape planning and climate resilience.

Abstract

In response to climate change and urban heat island effects, enhancing human thermal comfort in cities is crucial for sustainable urban development. Traditional methods for investigating the urban thermal environment and corresponding human thermal comfort level are often resource intensive, inefficient, and limited in scope. To address these challenges, we (1) introduce a new concept named thermal affordance, which formalizes the integrated inherent capacity of a streetscape to influence human thermal comfort based on its visual and physical features; and (2) an efficient method to evaluate it (visual assessment of thermal affordance -- VATA), which combines street view imagery (SVI), online and in-field surveys, and statistical learning algorithms. VATA extracts five categories of image features from SVI data and establishes 19 visual-perceptual indicators for streetscape visual assessment. Using a multi-task neural network and elastic net regression, we model their chained relationship to predict and comprehend thermal affordance for Singapore. VATA predictions are validated with field-investigated OTC data, providing a cost-effective, scalable, and transferable method to assess the thermal comfort potential of urban streetscape. Moreover, we demonstrate its utility by generating a geospatially explicit mapping of thermal affordance, outlining a model update workflow for long-term urban-scale analysis, and implementing a two-stage prediction and inference approach (IF-VPI-VATA) to guide future streetscape improvements. This framework can inform streetscape design to support sustainable, liveable, and resilient urban environments.

Figures (22)

• Figure 1: VATA conceptual framework: from thermal affordance to its visual assessment. Source of the SVI: Google Street View.
• Figure 2: Visual Assessment of Thermal Affordance (VATA) framework: from challenges to a solution.
• Figure 3: Research framework based on IFs from SVI data, VPIs from online SVI visual assessment survey, and a two-stage MTNNL model for VATA prediction.
• Figure 4: Details on indicator scoring and data augmentation in the SVI visual assessment survey.
• Figure 5: SVI clustering based on segmentation results, and selection process for 500 SVIs taken for the online SVI visual assessment survey.