GeoAI - Accelerating a virtuous cycle between AI and Geo

Lokendra P.S. Chauhan; Shashi Shekhar

doi:10.1145/3474124.3474179

GeoAI - Accelerating a virtuous cycle between AI and Geo

Lokendra P.S. Chauhan, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Geospatial artificial intelligence (GeoAI) is the amalgamation of artificial intelligence (AI) with spatial computing to develop a better understanding of the physical world around us at the levels of an individual, communities, cities, nations, and the planet using geospatial data. The current applications of GeoAI vary from solving challenges in the fields of sustainable development, urban planning, supply chains, and navigation to defense, epidemiology, insurance, and the emerging on-demand economy. It is a highly interdisciplinary field that uses ideas from computing, geography, spatial data science, statistics, and engineering. The unique features of geospatial data like autocorrelation, geometric nature, and multiple modalities requires the AI algorithms to be developed in a fundamentally different way, which also expands the capabilities of AI in general. Considering the exponential increasing geospatial data and application, this confluence of AI and geospatial will become a virtuous cycle of growth for both AI and Geo.

Original language	English (US)
Title of host publication	2021 13th International Conference on Contemporary Computing, IC3 2021
Publisher	Association for Computing Machinery
Pages	355-370
Number of pages	16
ISBN (Electronic)	9781450389204
DOIs	https://doi.org/10.1145/3474124.3474179
State	Published - Aug 5 2021
Externally published	Yes
Event	13th International Conference on Contemporary Computing, IC3 2021 - Virtual, Online, India Duration: Aug 5 2021 → Aug 7 2021

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	13th International Conference on Contemporary Computing, IC3 2021
Country/Territory	India
City	Virtual, Online
Period	8/5/21 → 8/7/21

Bibliographical note

Funding Information:
This work was supported by National Science Foundation (1737633, 1901099, 1916518 and 2040459), and USDA (2017-51181-27222). We would like to express our gratitude to Kim Koffolt from the spatial computing group at the University of Minnesota for her helpful comments and refinements that significantly improved the readability of this article. We would also like to thank World Geospatial Industry Council (WGIC) for a variety of contributions through their report on AI/ML applications and policies for geospatial industry.

Publisher Copyright:
© 2021 Association for Computing Machinery. All rights reserved.

Keywords

Artificial Intelligence
Embodied Intelligence
GIS
GeoAI
Geospatial
Remote Sensing
Spatial
Sustainable Development

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access

10.1145/3474124.3474179

OpenUrl availability

Full text

Cite this

Chauhan, LPS & Shekhar, S 2021, GeoAI - Accelerating a virtuous cycle between AI and Geo. in 2021 13th International Conference on Contemporary Computing, IC3 2021. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 355-370, 13th International Conference on Contemporary Computing, IC3 2021, Virtual, Online, India, 8/5/21. https://doi.org/10.1145/3474124.3474179

@inproceedings{a3e91c1ba510455e8f8a52b9043a2b90,

title = "GeoAI - Accelerating a virtuous cycle between AI and Geo",

abstract = "Geospatial artificial intelligence (GeoAI) is the amalgamation of artificial intelligence (AI) with spatial computing to develop a better understanding of the physical world around us at the levels of an individual, communities, cities, nations, and the planet using geospatial data. The current applications of GeoAI vary from solving challenges in the fields of sustainable development, urban planning, supply chains, and navigation to defense, epidemiology, insurance, and the emerging on-demand economy. It is a highly interdisciplinary field that uses ideas from computing, geography, spatial data science, statistics, and engineering. The unique features of geospatial data like autocorrelation, geometric nature, and multiple modalities requires the AI algorithms to be developed in a fundamentally different way, which also expands the capabilities of AI in general. Considering the exponential increasing geospatial data and application, this confluence of AI and geospatial will become a virtuous cycle of growth for both AI and Geo.",

keywords = "Artificial Intelligence, Embodied Intelligence, GIS, GeoAI, Geospatial, Remote Sensing, Spatial, Sustainable Development",

author = "Chauhan, {Lokendra P.S.} and Shashi Shekhar",

note = "Funding Information: This work was supported by National Science Foundation (1737633, 1901099, 1916518 and 2040459), and USDA (2017-51181-27222). We would like to express our gratitude to Kim Koffolt from the spatial computing group at the University of Minnesota for her helpful comments and refinements that significantly improved the readability of this article. We would also like to thank World Geospatial Industry Council (WGIC) for a variety of contributions through their report on AI/ML applications and policies for geospatial industry. Publisher Copyright: {\textcopyright} 2021 Association for Computing Machinery. All rights reserved.; 13th International Conference on Contemporary Computing, IC3 2021 ; Conference date: 05-08-2021 Through 07-08-2021",

year = "2021",

month = aug,

day = "5",

doi = "10.1145/3474124.3474179",

language = "English (US)",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "355--370",

booktitle = "2021 13th International Conference on Contemporary Computing, IC3 2021",

}

TY - GEN

T1 - GeoAI - Accelerating a virtuous cycle between AI and Geo

AU - Chauhan, Lokendra P.S.

AU - Shekhar, Shashi

N1 - Funding Information: This work was supported by National Science Foundation (1737633, 1901099, 1916518 and 2040459), and USDA (2017-51181-27222). We would like to express our gratitude to Kim Koffolt from the spatial computing group at the University of Minnesota for her helpful comments and refinements that significantly improved the readability of this article. We would also like to thank World Geospatial Industry Council (WGIC) for a variety of contributions through their report on AI/ML applications and policies for geospatial industry. Publisher Copyright: © 2021 Association for Computing Machinery. All rights reserved.

PY - 2021/8/5

Y1 - 2021/8/5

N2 - Geospatial artificial intelligence (GeoAI) is the amalgamation of artificial intelligence (AI) with spatial computing to develop a better understanding of the physical world around us at the levels of an individual, communities, cities, nations, and the planet using geospatial data. The current applications of GeoAI vary from solving challenges in the fields of sustainable development, urban planning, supply chains, and navigation to defense, epidemiology, insurance, and the emerging on-demand economy. It is a highly interdisciplinary field that uses ideas from computing, geography, spatial data science, statistics, and engineering. The unique features of geospatial data like autocorrelation, geometric nature, and multiple modalities requires the AI algorithms to be developed in a fundamentally different way, which also expands the capabilities of AI in general. Considering the exponential increasing geospatial data and application, this confluence of AI and geospatial will become a virtuous cycle of growth for both AI and Geo.

AB - Geospatial artificial intelligence (GeoAI) is the amalgamation of artificial intelligence (AI) with spatial computing to develop a better understanding of the physical world around us at the levels of an individual, communities, cities, nations, and the planet using geospatial data. The current applications of GeoAI vary from solving challenges in the fields of sustainable development, urban planning, supply chains, and navigation to defense, epidemiology, insurance, and the emerging on-demand economy. It is a highly interdisciplinary field that uses ideas from computing, geography, spatial data science, statistics, and engineering. The unique features of geospatial data like autocorrelation, geometric nature, and multiple modalities requires the AI algorithms to be developed in a fundamentally different way, which also expands the capabilities of AI in general. Considering the exponential increasing geospatial data and application, this confluence of AI and geospatial will become a virtuous cycle of growth for both AI and Geo.

KW - Artificial Intelligence

KW - Embodied Intelligence

KW - GIS

KW - GeoAI

KW - Geospatial

KW - Remote Sensing

KW - Spatial

KW - Sustainable Development

UR - http://www.scopus.com/inward/record.url?scp=85119091888&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85119091888&partnerID=8YFLogxK

U2 - 10.1145/3474124.3474179

DO - 10.1145/3474124.3474179

M3 - Conference contribution

AN - SCOPUS:85119091888

T3 - ACM International Conference Proceeding Series

SP - 355

EP - 370

BT - 2021 13th International Conference on Contemporary Computing, IC3 2021

PB - Association for Computing Machinery

T2 - 13th International Conference on Contemporary Computing, IC3 2021

Y2 - 5 August 2021 through 7 August 2021

ER -

GeoAI - Accelerating a virtuous cycle between AI and Geo

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this