Assessing walkability with deep CNNs by integrating objective and subjective urban qualities: The case of ‘Cittaslow’ neighborhoods

IF 12 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Sena Kaynarkaya , Aslı Çekmiş , İsmail Çetin , Yusuf Hüseyin Şahin , Gözde Ünal
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Abstract

Walkability, shaped by features of built environment, contributes to healthier communities and more sustainable urban mobility. Well-designed streetscapes encourage pedestrian activity, making individuals’ perceptions of safety, accessibility, and aesthetics central to understanding walkable environments. Existing studies typically evaluate walkability through either objective spatial data or perceptual assessments based on human experiences. However, comprehensive approaches that combines both perspectives remain limited. This study presents an AI-driven framework for evaluating urban walkability by integrating objective built environment features with subjective perceptions in the context of Cittaslow-certified neighborhoods. The research employs the Segment Anything Model 2(SAM2) for high-resolution and class-agnostic segmentation of street-level imagery. It is created: “Urban Walkability Dataset” (UWD) which contains 5,440 labeled images by experts with a question set generated based on the key parameters affecting walkability. A neural network pipeline is designed to understand the underlying process. By bridging perceptual insights and objective metrics, this research contributes a replicable methodology for walkability assessment that supports human-centered urban design strategies, particularly in slow-city contexts prioritizing sustainability and quality of life.
通过综合客观和主观的城市品质,用深度cnn评估可步行性:以“慢城”社区为例
由建筑环境特征塑造的可步行性有助于建立更健康的社区和更可持续的城市流动性。精心设计的街道景观鼓励行人活动,使个人对安全、可达性和美学的感知成为理解可步行环境的核心。现有研究通常通过客观空间数据或基于人类经验的感知评估来评估步行性。然而,结合这两种观点的综合方法仍然有限。本研究提出了一个人工智能驱动的框架,通过将客观建成环境特征与“慢城”认证社区的主观感知相结合,来评估城市步行性。该研究采用分段任意模型2(SAM2)对街道图像进行高分辨率和类别无关的分割。它是由“城市步行能力数据集”(UWD)创建的,该数据集包含5,440张由专家标记的图像,并根据影响步行能力的关键参数生成问题集。一个神经网络管道被设计用来理解底层的过程。通过连接感知洞察和客观指标,本研究为步行性评估提供了可复制的方法,支持以人为本的城市设计策略,特别是在慢城市背景下优先考虑可持续性和生活质量。
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society Social Sciences-Geography, Planning and Development
CiteScore
22.00
自引率
13.70%
发文量
810
审稿时长
27 days
期刊介绍: Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;
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