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Title:           Quantifying the accuracy of OpenStreetMap for longitudinal cycling-infrastructure change: A reproducible validation framework using Google Street View
Subject:         Journal of Transport Geography, 136 (2026) 104775. doi:10.1016/j.jtrangeo.2026.104775
Keywords:        OpenStreetMap (OSM),Cycling infrastructure,Urban data science,Change detection,Spatial validation,Reproducible workflows
Author:          Eugeni Vidal-Tortosa
Creator:         Elsevier
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     Item: 
      Title: Quantifying the accuracy of OpenStreetMap for longitudinal cycling-infrastructure change: A reproducible validation framework using Google Street View
      Children: 
       Item: 
        Title: Introduction
       Item: 
        Title: Data and methods
        Children: 
         Item: 
          Title: Study setting
         Item: 
          Title: Data sources
         Item: 
          Title: Analytical workflow
       Item: 
        Title: Results
        Children: 
         Item: 
          Title: Network change detected from OSM (2015–2023)
         Item: 
          Title: Validation of OSM-detected changes using GSV
         Item: 
          Title: Sensitivity to geometric parameters
       Item: 
        Title: Discussion
       Item: 
        Title: Generalisability and limitations
       Item: 
        Title: Conclusions
       Item: 
        Title: CRediT authorship contribution statement
       Item: 
        Title: Declaration of Generative AI and AI-assisted technologies in the writing process
       Item: 
        Title: Declaration of competing interest
       Item: 
        Title: Acknowledgements
       Item: 
        Title: . Supplements
       Item: 
        Title: Data availability
       Item: 
        Title: References
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