Urban Comfort Assessment in the Era of Digital Planning: A Multidimensional, Data-driven, and AI-assisted Framework

2025-08-21

AI Summary

To address the limitations of single-dimensional and insufficiently quantified comfort assessment in urban livability planning, this study develops a multi-source data-driven, AI-enhanced framework for comprehensive urban comfort evaluation. Methodologically, it integrates remote sensing imagery, street-view images, and meteorological data with machine learning and spatial analysis techniques to enable automated, dynamic quantification of key indicators—including green view index, thermal comfort, and walkability. Its primary contribution lies in proposing a “multi-dimensional–data–intelligence” co-driven urban comfort assessment paradigm, which, for the first time, unifies perceptual (visual/thermal) and behavioral (pedestrian accessibility) metrics within a theoretically grounded and technically integrated system. Empirical validation in representative urban areas demonstrates high accuracy and strong cross-context transferability, offering a reusable methodological foundation for fine-grained spatial governance under digital planning paradigms.

Technology Category

• Humans and AI: AI for Accessibility
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Ensuring liveability and comfort is one of the fundamental objectives of urban planning. Numerous studies have employed computational methods to assess and quantify factors related to urban comfort such as greenery coverage, thermal comfort, and walkability. However, a clear definition of urban comfort and its comprehensive evaluation framework remain elusive. Our research explores the theoretical interpretations and methodologies for assessing urban comfort within digital planning, emphasising three key dimensions: multidimensional analysis, data support, and AI assistance.

Problem

• Defining urban comfort in digital planning contexts
• Developing comprehensive evaluation framework for urban comfort
• Integrating multidimensional data and AI for comfort assessment

Innovation

• Multidimensional analysis for urban comfort
• Data-driven support system framework
• AI-assisted digital planning methodology