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

2024-10-12

arXiv.org

AI Summary

To address the high cost and poor scalability of street-level thermal comfort assessment under urban heat island effects, this study proposes the concept of “thermal validity”—the capacity of street-view visual and physical features to jointly influence human thermal comfort—and develops a vision-based assessment method (VATA). Methodologically, it integrates street-view image analysis, multi-source questionnaire surveys, geospatial mapping, and iterative model refinement. Key contributions include: (1) defining 19 interpretable visual-perceptual indicators; (2) designing a hybrid modeling framework combining a multi-task neural network with elastic net regression; and (3) implementing a two-stage inference pipeline (IF-VPI-VATA) to support design optimization. Empirical validation in Singapore demonstrates strong agreement between VATA predictions and field-measured outdoor thermal comfort (OTC) indices (R² > 0.85), enabling generation of meter-scale thermal validity distribution maps. This provides a quantitative, scalable foundation for designing sustainable and climate-resilient streetscapes.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Enhancing urban thermal comfort for sustainability
• Introducing thermal affordance for streetscape assessment
• Developing VATA method for efficient thermal evaluation

Innovation

• Visual assessment of thermal affordance
• Combines street view and surveys
• Uses multi-task neural network