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

Yifeng Sun; Xianfei Yin; Jianyu Yin; Xue Chen; Chi Chiu Lam

doi:10.1007/978-3-031-97701-5_14

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

Yifeng Sun, Xianfei Yin^*, Jianyu Yin, Xue Chen, Chi Chiu Lam

^*Corresponding author for this work

Department of Architecture and Civil Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

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. © Canadian Society for Civil Engineering 2026.

Original language	English
Title of host publication	Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 2 - Engineering Management
Editors	Pouya Zangeneh, Farnaz Sadeghpour, Clare Robinson
Publisher	Springer, Cham
Pages	197-209
Number of pages	13
ISBN (Electronic)	9783031977015
ISBN (Print)	9783031977008
DOIs	https://doi.org/10.1007/978-3-031-97701-5_14
Publication status	Published - Jun 2024
Event	Canadian Society of Civil Engineering Annual Conference 2024 (CSCE 2024) - Niagara Falls, Canada Duration: 5 Jun 2024 → 7 Jun 2024 https://www.csce2024niagara.ca/

Publication series

Name	Lecture Notes in Civil Engineering
Volume	698
ISSN (Print)	2366-2557
ISSN (Electronic)	2366-2565

Conference

Conference	Canadian Society of Civil Engineering Annual Conference 2024 (CSCE 2024)
Place	Canada
City	Niagara Falls
Period	5/06/24 → 7/06/24
Internet address	https://www.csce2024niagara.ca/

Research Keywords

Bibliometric analysis
Geographic information system
Literature review
Machine learning

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10.1007/978-3-031-97701-5_14

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Sun, Y., Yin, X., Yin, J., Chen, X., & Lam, C. C. (2024). Machine Learning and Geographic Information System Synergies: A Literature Review and Future Opportunities. In P. Zangeneh, F. Sadeghpour, & C. Robinson (Eds.), Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 2 - Engineering Management (pp. 197-209). (Lecture Notes in Civil Engineering; Vol. 698). Springer, Cham. https://doi.org/10.1007/978-3-031-97701-5_14

Sun, Yifeng ; Yin, Xianfei ; Yin, Jianyu et al. / Machine Learning and Geographic Information System Synergies : A Literature Review and Future Opportunities. Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 2 - Engineering Management. editor / Pouya Zangeneh ; Farnaz Sadeghpour ; Clare Robinson. Springer, Cham, 2024. pp. 197-209 (Lecture Notes in Civil Engineering).

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title = "Machine Learning and Geographic Information System Synergies: A Literature Review and Future Opportunities",

abstract = "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. {\textcopyright} Canadian Society for Civil Engineering 2026.",

keywords = "Bibliometric analysis, Geographic information system, Literature review, Machine learning",

author = "Yifeng Sun and Xianfei Yin and Jianyu Yin and Xue Chen and Lam, {Chi Chiu}",

year = "2024",

month = jun,

doi = "10.1007/978-3-031-97701-5_14",

language = "English",

isbn = "9783031977008",

series = "Lecture Notes in Civil Engineering",

publisher = "Springer, Cham",

pages = "197--209",

editor = "Pouya Zangeneh and Farnaz Sadeghpour and Clare Robinson",

booktitle = "Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 2 - Engineering Management",

note = "Canadian Society of Civil Engineering Annual Conference 2024 (CSCE 2024) ; Conference date: 05-06-2024 Through 07-06-2024",

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Sun, Y, Yin, X, Yin, J, Chen, X & Lam, CC 2024, Machine Learning and Geographic Information System Synergies: A Literature Review and Future Opportunities. in P Zangeneh, F Sadeghpour & C Robinson (eds), Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 2 - Engineering Management. Lecture Notes in Civil Engineering, vol. 698, Springer, Cham, pp. 197-209, Canadian Society of Civil Engineering Annual Conference 2024 (CSCE 2024), Niagara Falls, Ontario, Canada, 5/06/24. https://doi.org/10.1007/978-3-031-97701-5_14

Machine Learning and Geographic Information System Synergies: A Literature Review and Future Opportunities. / Sun, Yifeng; Yin, Xianfei; Yin, Jianyu et al.
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 2 - Engineering Management. ed. / Pouya Zangeneh; Farnaz Sadeghpour; Clare Robinson. Springer, Cham, 2024. p. 197-209 (Lecture Notes in Civil Engineering; Vol. 698).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Machine Learning and Geographic Information System Synergies

T2 - Canadian Society of Civil Engineering Annual Conference 2024 (CSCE 2024)

AU - Sun, Yifeng

AU - Yin, Xianfei

AU - Yin, Jianyu

AU - Chen, Xue

AU - Lam, Chi Chiu

PY - 2024/6

Y1 - 2024/6

N2 - 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. © Canadian Society for Civil Engineering 2026.

AB - 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. © Canadian Society for Civil Engineering 2026.

KW - Bibliometric analysis

KW - Geographic information system

KW - Literature review

KW - Machine learning

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-105029542456&origin=recordpage

U2 - 10.1007/978-3-031-97701-5_14

DO - 10.1007/978-3-031-97701-5_14

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783031977008

T3 - Lecture Notes in Civil Engineering

SP - 197

EP - 209

BT - Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 2 - Engineering Management

A2 - Zangeneh, Pouya

A2 - Sadeghpour, Farnaz

A2 - Robinson, Clare

PB - Springer, Cham

Y2 - 5 June 2024 through 7 June 2024

ER -

Sun Y, Yin X, Yin J, Chen X, Lam CC. Machine Learning and Geographic Information System Synergies: A Literature Review and Future Opportunities. In Zangeneh P, Sadeghpour F, Robinson C, editors, Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 2 - Engineering Management. Springer, Cham. 2024. p. 197-209. (Lecture Notes in Civil Engineering). doi: 10.1007/978-3-031-97701-5_14

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

Abstract

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