arXiv Machine Learning By Houhao Liang, Kresimir Friganovic, Joanne Kua, Noor Hafizah Ismail, Su Su, Bryan Yijia Tan, Navrag B. Singh, Panos Mavros

In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults

Read the original on arXiv Machine Learning →

arXiv:2608. 14663v1 Announce Type: new Abstract: As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers that discourage physical activity and social participation among older adults.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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When Shortest Isn't Safest: A Design Science Approach to Senior-Friendly Pedestrian Routing

arXiv:2607. 24795v1 Announce Type: new Abstract: Older adults' independent mobility enables out-of-home participation, well-being and health, yet pedestrian navigation systems still optimize primarily for distance or time, often overlooking barriers, safety thresholds, and supportive infrastructure that shape late-life walking decisions.

By Erdi \"Unal, Daniel Eisenhardt, Christian Meske, Seyed Nima Afzali, Ayseg\"ul Dogang\"un
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How Does Urban Context Relate to Residential Building Health? A Vision-POI Fusion Framework for Building-Level Housing Inspection

Housing-level urban physical examination is essential for identifying residential building problems and supporting targeted urban renewal. Existing automated inspection studies primarily rely on individual images and rarely examine whether surrounding urban functional context can provide supplementary information for building-level assessment.