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

TL;DR

This paper introduces a visual assessment method called VATA for evaluating thermal affordance in urban streetscapes, enabling scalable and cost-effective predictions of human thermal comfort to support sustainable urban design.

Contribution

It formalizes the concept of thermal affordance and develops VATA, an innovative, efficient method combining imagery, surveys, and machine learning for assessing urban thermal comfort.

Findings

VATA accurately predicts thermal affordance using street view imagery.

The method is validated with field data, showing high correlation.

Generated geospatial maps assist in urban planning for thermal comfort.

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.