Thermal Affordance | Sijie Yang

Streetscapes shape how hot or comfortable a city feels, yet measuring that potential across whole neighbourhoods has long been slow, costly, and tied to field campaigns. Thermal Affordance asks whether the visual information already embedded in street view imagery can serve as a scalable proxy for thermal comfort potential—linking what people see to what they are likely to experience outdoors.

We introduce thermal affordance as the integrated capacity of a streetscape—through its visible geometry, greenery, shading, and material cues—to support or hinder human thermal comfort. The Visual Assessment of Thermal Affordance (VATA) framework combines street view imagery, online and in-field surveys, and statistical learning: image features (IF) and visual-perceptual indicators (VPI) are chained to predict affordance scores, then validated against on-the-ground comfort measurements.

The approach is designed to be cost-effective, city-scale, and interpretable. Beyond prediction, VATA supports geospatial mapping of thermal comfort potential, model updating for long-term monitoring, and a two-stage inference workflow (IF–VPI–VATA) to guide streetscape improvements—informing design toward more sustainable, liveable, and resilient urban environments.

This work sits within the broader City in Sight programme as the street-level channel for linking visible urban form to human-centred environmental experience.

Related empirical work is listed below.

街景塑造着城市有多热、多舒适的主观感受，但在整个片区尺度上测量这种潜力，长期依赖缓慢、昂贵的外业调查。热舒适可供性研究：街景影像中已有的视觉信息，能否作为热舒适潜力的可扩展代理——连接人们所见与他们可能在户外体验到什么。

我们将 thermal affordance 定义为：街景通过可见的几何、绿化、阴影与材质线索，所支持或阻碍人类热舒适的整体能力。Visual Assessment of Thermal Affordance（VATA） 框架结合街景、在线与外业调查及统计学习：图像特征（IF）与视觉—感知指标（VPI）链式预测可供性得分，并与实地舒适测量交叉验证。

该方法旨在低成本、城市尺度、可解释。除预测外，VATA 支持热舒适潜力的地理制图、长期监测的模型更新，以及两阶段推断流程（IF–VPI–VATA）以指导街景改善——为更可持续、宜居、有韧性的建成环境提供设计依据。

本工作隶属于更广泛的 城市在眼前 研究线，作为连接可见城市形态与以人为中心环境体验的街道尺度通道。

相关实证工作见下文。

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.

2025