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The recent rapid advancements of Machine Learning (ML) have significantly empowered and transformed many research domains, including the architecture, engineering, and construction (AEC) area. Meanwhile, Geographic Information System (GIS) is also a commonly used tool. In order to better understand the state-of-the-art ML and GIS synergies, this research conducts a bibliometric analysis to reveal hidden information from relevant literature and explore the integration cases of ML and GIS. As a result, a dataset of 3387 relevant articles (including articles and conference papers) published from 2010 to 2023 was retrieved from Scopus and further analyzed. The research employs visualization techniques to highlight key publications, active research institutions, key researchers, influential journals, etc. Several research themes have been identified as the most studied areas. As a result, the data obtained from this study will provide valuable information to support future research.

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Machine Learning and Geographic Information System Synergies: A Literature Review and Future Opportunities

  • 会议论文

  • 发表日期：

    2026年

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  • Yifeng Sun,
  • Xianfei Yin,
  • Jianyu Yin,
  • Xue Chen,
  • Chi Chiu Lam

摘要

The recent rapid advancements of Machine Learning (ML) have significantly empowered and transformed many research domains, including the architecture, engineering, and construction (AEC) area. Meanwhile, Geographic Information System (GIS) is also a commonly used tool. In order to better understand the state-of-the-art ML and GIS synergies, this research conducts a bibliometric analysis to reveal hidden information from relevant literature and explore the integration cases of ML and GIS. As a result, a dataset of 3387 relevant articles (including articles and conference papers) published from 2010 to 2023 was retrieved from Scopus and further analyzed. The research employs visualization techniques to highlight key publications, active research institutions, key researchers, influential journals, etc. Several research themes have been identified as the most studied areas. As a result, the data obtained from this study will provide valuable information to support future research.