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<div><p>Urban environments significantly shape our well-being, behavior, and overall quality of life. Assessing urban environments, particularly walkability, has traditionally relied on computer vision and machine learning algorithms. However, these approaches often fail to capture the s...

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Bibliographic Details
Main Author:	Musab Wedyan (21212449) (author)
Other Authors:	Yu-Chen Yeh (5337155) (author), Fatemeh Saeidi-Rizi (8490228) (author), Tai-Quan Peng (3637114) (author), Chun-Yen Chang (679953) (author)
Published:	2025
Subjects:	Biotechnology Sociology Cancer Science Policy Mental Health Biological Sciences not elsewhere classified text mining techniques notable differences emerged machine learning algorithms large language models examining keyword frequency approaches often fail level images based visual urban scenes 4o evaluating street 4o &# 8217 fully capturing human critical question arises assessing urban environments future walkability studies including overall walkability analyze unstructured data urban studies urban environments urban walkability overall walkability moderate level overall quality friendly environments integrate human human perceptions human judgments wider range traditionally relied study concludes study aims similarity scores similarity indices key dimensions increasing reliance gained traction findings revealed emotional dimensions different lenses critically evaluate contextual understanding computer vision comparative study coherence scores centered perceptions better align
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