GeoAI: Where machine learning and big data converge in GIScience

Wenwen Li

doi:10.5311/JOSIS.2020.20.658

GeoAI: Where machine learning and big data converge in GIScience

Wenwen Li

Geographical Sciences and Urban Planning, School of (SGSUP)

Research output: Contribution to journal › Article › peer-review

73 Scopus citations

Abstract

In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIScience. As the new fuel of geospatial research, GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable processing and intelligent analysis of geospatial big data. The three-pillar view of GeoAI, its two methodological threads (data-driven and knowledge-driven), as well as their geospatial applications are highlighted. The paper concludes with discussion of remaining challenges and future research directions of GeoAI.

Original language	English (US)
Pages (from-to)	71-77
Number of pages	7
Journal	Journal of Spatial Information Science
Volume	20
DOIs	https://doi.org/10.5311/JOSIS.2020.20.658
State	Published - 2020

Keywords

Big data
CNN
Convergence research
Data-driven
Deep learning
GeoAI
Knowledge graph

ASJC Scopus subject areas

Information Systems
Geography, Planning and Development
Computers in Earth Sciences

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10.5311/JOSIS.2020.20.658

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abstract = "In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIScience. As the new fuel of geospatial research, GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable processing and intelligent analysis of geospatial big data. The three-pillar view of GeoAI, its two methodological threads (data-driven and knowledge-driven), as well as their geospatial applications are highlighted. The paper concludes with discussion of remaining challenges and future research directions of GeoAI.",

keywords = "Big data, CNN, Convergence research, Data-driven, Deep learning, GeoAI, Knowledge graph",

author = "Wenwen Li",

note = "Funding Information: This research was in part supported by National Science Foundation, grant number 1853864, 1455349, 1937908, and 1936677. Publisher Copyright: {\textcopyright} by the author(s).",

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AU - Li, Wenwen

N1 - Funding Information: This research was in part supported by National Science Foundation, grant number 1853864, 1455349, 1937908, and 1936677. Publisher Copyright: © by the author(s).

PY - 2020

Y1 - 2020

N2 - In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIScience. As the new fuel of geospatial research, GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable processing and intelligent analysis of geospatial big data. The three-pillar view of GeoAI, its two methodological threads (data-driven and knowledge-driven), as well as their geospatial applications are highlighted. The paper concludes with discussion of remaining challenges and future research directions of GeoAI.

AB - In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIScience. As the new fuel of geospatial research, GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable processing and intelligent analysis of geospatial big data. The three-pillar view of GeoAI, its two methodological threads (data-driven and knowledge-driven), as well as their geospatial applications are highlighted. The paper concludes with discussion of remaining challenges and future research directions of GeoAI.

KW - Big data

KW - CNN

KW - Convergence research

KW - Data-driven

KW - Deep learning

KW - GeoAI

KW - Knowledge graph

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GeoAI: Where machine learning and big data converge in GIScience

Abstract

Keywords

ASJC Scopus subject areas

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