Application of deep learning for symbol detection on historical maps to explore spatiotemporal changes in the regional tea industry of early 20th-century Taiwan.

Focusing on the early history of the tea industry in Tamsui, Taiwan, this study uses land used data extracted from maps of the 1900s and 1920s to explore the regional characteristics and changes in the distribution of tea plantations. Map symbol detection modeling was performed using Artificial Inte...

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Publicado en:Digital Scholarship in the Humanities Vol. 40; no. 4; pp. 1243 - 1261
Autores principales: Pai, Pi-Ling, Liu, Chan-Yu, Kuo, Chiao-Ling, Chan, Ta-Chien
Formato: Artículo
Publicado: Oxford University Press / USA Dec2025
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Application of deep learning for symbol detection on historical maps to explore spatiotemporal changes in the regional tea industry of early 20th-century Taiwan.
      aug:
        au:
          Pai, Pi-Ling
          Liu, Chan-Yu
          Kuo, Chiao-Ling
          Chan, Ta-Chien
        affil:
          Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 115, Taiwan
          Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 115, TaiwanMS in Data Science, Boston University, One Silber Way, Boston, MA02215, United States
      su:
        Deep learning
        Historical maps
        Geographic spatial analysis
        Spatiotemporal processes
        Taiwanese people
        Pattern perception
        Twentieth century
        Tea trade
        Taiwan
      sug:
        subj:
          Taiwan
          Deep learning
          Historical maps
          Geographic spatial analysis
          Spatiotemporal processes
          Taiwanese people
          Pattern perception
          Twentieth century
          Tea trade
      keyword:
        deep learning
        historical GIS
        historical map
        spatiotemporal analysis
        symbol detection
      ab: Focusing on the early history of the tea industry in Tamsui, Taiwan, this study uses land used data extracted from maps of the 1900s and 1920s to explore the regional characteristics and changes in the distribution of tea plantations. Map symbol detection modeling was performed using Artificial Intelligence deep learning techniques, which have been growing in the field of map research in recent years. Through the constructed symbol detection model, the land use annotation data of historical maps can be automatically retrieved for GIS-based spatiotemporal analysis. Thus, the study presents the impact of global economic panic and the failure of tea exportation in the 1920s on the local tea industry and reflects the tea plantation landscape in response strategies.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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