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GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions

Authors:

Budhendra BhaduriAuthors Info & Claims

SIGSPATIAL Special, Volume 11, Issue 2

Pages 5 - 15

https://doi.org/10.1145/3377000.3377002

Published: 17 December 2019 Publication History

Abstract

Geospatial artificial intelligence (GeoAI) is an interdisciplinary field that has received tremendous attention from both academia and industry in recent years. This article reviews the series of GeoAI workshops held at the Association for Computing Machinery (ACM) International Conference on Advances in Geographic Information Systems (SIGSPATIAL) since 2017. These workshops have provided researchers a forum to present GeoAI advances covering a wide range of topics, such as geospatial image processing, transportation modeling, public health, and digital humanities. We provide a summary of these topics and the research articles presented at the 2017, 2018, and 2019 GeoAI workshops. We conclude with a list of open research directions for this rapidly advancing field.

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Cited By

Wetzel SMäs S(2024)Context-Aware Search for Environmental Data Using Dense RetrievalISPRS International Journal of Geo-Information10.3390/ijgi1311038013:11(380)Online publication date: 30-Oct-2024
https://doi.org/10.3390/ijgi13110380
Lee WLauw H(2024)Latent Representation Learning for Geospatial EntitiesACM Transactions on Spatial Algorithms and Systems10.1145/366347410:4(1-31)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1145/3663474
Hu YGoodchild MZhu AYuan MAydin OBhaduri BGao SLi WLunga DNewsam S(2024)A five-year milestone: reflections on advances and limitations in GeoAI researchAnnals of GIS10.1080/19475683.2024.230986630:1(1-14)Online publication date: 29-Jan-2024
https://doi.org/10.1080/19475683.2024.2309866
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Published In

cover image SIGSPATIAL Special

SIGSPATIAL Special Volume 11, Issue 2

July 2019

39 pages

EISSN:1946-7729

DOI:10.1145/3377000

Editor:
Andreas Züfle
George Mason University

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Copyright © 2019 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 17 December 2019

Published in SIGSPATIAL Volume 11, Issue 2

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Cited By

Wetzel SMäs S(2024)Context-Aware Search for Environmental Data Using Dense RetrievalISPRS International Journal of Geo-Information10.3390/ijgi1311038013:11(380)Online publication date: 30-Oct-2024
https://doi.org/10.3390/ijgi13110380
Lee WLauw H(2024)Latent Representation Learning for Geospatial EntitiesACM Transactions on Spatial Algorithms and Systems10.1145/366347410:4(1-31)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1145/3663474
Hu YGoodchild MZhu AYuan MAydin OBhaduri BGao SLi WLunga DNewsam S(2024)A five-year milestone: reflections on advances and limitations in GeoAI researchAnnals of GIS10.1080/19475683.2024.230986630:1(1-14)Online publication date: 29-Jan-2024
https://doi.org/10.1080/19475683.2024.2309866
Atkins CGirgente GShirzaei MKim J(2024)Generative AI tools can enhance climate literacy but must be checked for biases and inaccuraciesCommunications Earth & Environment10.1038/s43247-024-01392-w5:1Online publication date: 30-Apr-2024
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Abdel Magid HDesjardins MHu Y(2024)Opportunities and shortcomings of AI for spatial epidemiology and health disparities research on aging and the life courseHealth & Place10.1016/j.healthplace.2024.10332389(103323)Online publication date: Sep-2024
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Chen HJiang SYu XYin HWang XHu YWang CLi C(2024)GeoVis: a data-driven geographic visualization recommendation system via latent space encodingJournal of Visualization10.1007/s12650-024-00986-y27:4(603-622)Online publication date: 16-Apr-2024
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