AAG 2024 GeoAI Symposium

Dear colleagues,

We cannot wait to take our AAG 2024 GeoAI Symposium to Hawaii next year! Collaborating with 40+ colleagues across multiple continents, we have put together a series of paper and panel sessions. In the past year, we have been so excited to witness the rapid and continued growth of GeoAI, the advances in its methods and cross-domain applications. This year’s symposium will highlight these advances and will also include critical discussions on the issues of GeoAI and the societal challenges in its use in science and everyday life.

We welcome you to join us to present your papers, co-organize sessions, and serve as a panelist in our symposium. Your participation is key to helping us expand this exciting research community! If you have any questions, please feel free to reach out to the symposium’s lead organizers. The CFP can be found in the attachment.


AAG 2024 GeoAI Symposium organizing team

Lead Organizers:
Wenwen Li, Arizona State University 
Yingjie Hu, University at Buffalo
Song Gao, University of Wisconsin, Madison
Budhu Bhaduri, Oak Ridge National Laboratory
Orhun Aydin, Saint Louis University
Shawn Newsam, University of California, Merced 
Samantha T. Arundel, United States Geological Survey
Gengchen Mai, University of Georgia
Krzysztof Janowicz, University of Vienna & University of California, Santa Barbara

Sessions (all sessions can be accessed at: https://bit.ly/aag2024geoai): 

  • GeoAI and Deep Learning Symposium: GeoAI for Science and the Science of GeoAI (Panel discussion session; in-person session; The organizing team)
  • GeoAI and Deep Learning Symposium: GeoAI Foundation Models (Panel discussion session; in-person session; The organizing team)
  • GeoAI and Deep Learning Symposium: GeoAI for Feature Detection and Recognition (Paper session; In-person session; Contact: Sam Arundel, US Geological Survey; Co-organizer: Wenwen Li, Arizona State University)
  • GeoAI and Deep Learning Symposium: GeoAI for Spatial Analytics and Modeling (Paper session; In-person session; Contact: Di Zhu, University of Minnesota; Co-organizers: Guofeng Cao, University of Colorado, Boulder; Song Gao, University of Wisconsin, Madison)
  • GeoAI and Deep Learning Symposium: Emerging Geo-Data Applications in Human Mobility Analysis (Paper session; In-person session; Contact: Xiao Li, University of Oxford; Co-organizers: Xiao Huang, University of Arkansas, Haowen Xu, Oak Ridge National Laboratory, Yuhao Kang, University of South Carolina; Di Zhu, University of Minnesota)
  • GeoAI and Deep Learning Symposium: GeoAI for Ecosystem Conservation and  and Sustainable Geodesign (Contact: Orhun Aydin, Saint Louis University; Somayeh Dodge, University of California Santa Barbara) 
  • GeoAI and Deep Learning Symposium: GeoAI for Disaster Resilience I (Paper session; In-person session; Contact Bing Zhou, Texas A&M University. Co-organizers: Lei Zou, Texas A&M University; Yingjie Hu, University at Buffalo; Marcela Suárez, Penn State University, Yi Qiang, University of South Florida; Manzhu Yu, Penn State University; Morteza Karimzadeh, University of Colorado Boulder)
  • GeoAI and Deep Learning Symposium: Urban Visual Intelligence (Paper session; In-person session; Contact: Fan Zhang, Peking University, Co-organizer: Yuhao Kang, University of South Carolina; Filip Biljecki, National University of Singapore)
  • GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence (Paper session; In-person session; Contact: Gengchen Mai, University of Georgia; Co-organizers: Angela Yao, University of Georgia; Yao-Yi Chiang, University of Minnesota-Twin Cities; Krzysztof Janowicz, University of Vienna & UC Santa Barbara; Zhangyu Wang, University of California Santa Barbara; Di Zhu, University of Minnesota-Twin Cities)
  • GeoAI and Deep Learning Symposium: GeoAI for Cartography and Mapping (Paper session; In-person session; Contact: Yao-Yi Chiang, University of Minnesota-Twin Cities; Co-organizer: Jina Kim, University of Minnesota
  • GeoAI and Deep Learning Symposium: Responsible GeoAI: Privacy, Fairness, and Interpretability in Spatial Data Science  (Paper session; In-person session; Contact: Hongyu Zhang, McGill University; Co-organizers: Yue Lin, University of Chicago; Jinmeng Rao, Mineral Earth Sciences, Alphabet Inc.; Junghwan Kim, Virginia Tech; Song Gao, University of Wisconsin – Madison
  • GeoAI and Deep Learning Symposium: GeoAI for Sustainable and Computational Agriculture (Paper session; In-person session; Contact: Jinmeng Rao, Mineral Earth Sciences, Alphabet Inc.; Co-organizers: Yuchi Ma, Stanford University; Jiahao Fan, University of Wisconsin-Madison; Hongxu Ma, Mineral Earth Sciences, Alphabet Inc.; Gengchen Mai, University of Georgia; Di Zhu, University of Minnesota, Twin Cities)
  • GeoAI and Deep Learning Symposium: Human-centered Geospatial Data Science (Paper session; In-person session; Contact: Yuhao Kang, University of South Carolina; Co-organizers: Filip Biljecki, National University of Singapore)
  • GeoAI and Deep Learning Symposium: GeoAI and Social Sensing for Human-Pandemic Dynamics (Paper session; In-person session; Contact: Binbin Lin, Texas A&M University, Mingzheng Yang, Texas A&M University; Co-organizers: Lei Zou, Texas A&M University
  • GeoAI and Deep Learning Symposium: GeoHealth Data Science (Paper session; In-person session; Contact: Jiannan Cai, The Chinese University of Hong Kong; Co-organizer: Mei-Po Kwan, The Chinese University of Hong Kong)

To present your research in one of these sessions, please register and submit your abstract at https://aag.secure-platform.com/aag2024/. When you receive confirmation of your submission, please forward your confirmation email to the session organizers by Nov. 16, 2023.

Two vision papers about GeoAI Foundation Models accepted at SIGSPATIAL 2023

The 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023) will be held in Hamburg, Germany, Monday November 13 – Thursday November 16, 2023. This is the flagship international conference organized by the special interest group of SPATIAL at ACM.

GeoDS lab members have two vision papers about GeoAI Foundation Models (Geo-Foundation Models) accepted as oral presentations.

Jinmeng Rao, Song Gao, Gengchen Mai, Krzysztof Janowicz. (2023) Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models (Vision Paper).

Abstract: In recent years we have seen substantial advances in foundation models for artificial intelligence, including language, vision, and multimodal models. Recent studies have highlighted the potential of using foundation models in geospatial artificial intelligence, known as GeoAI Foundation Models or Geo-Foundation Models, for geographic question answering, remote sensing image understanding, map generation, and location-based services, among others. However, the development and application of GeoAI foundation models can pose serious privacy and security risks, which have not been fully discussed or addressed to date. This paper introduces the potential privacy and security risks throughout the lifecycle of GeoAI foundation models and proposes a comprehensive blueprint for preventative and control strategies. Through this vision paper, we hope to draw the attention of researchers and policymakers in geospatial domains to these privacy and security risks inherent in GeoAI foundation models and advocate for the development of privacy-preserving and secure GeoAI foundation models.

Yiqun Xie, Zhaonan Wang, Gengchen Mai, Yanhua Li, Xiaowei Jia, Song Gao and Shaowen Wang. (2023) “Geo”-Foundation Models: Reality, Gaps and Opportunities (Vision Paper).

Abstract: With the recent rapid advances of revolutionary AI models such as ChatGPT, foundation models have become a main topic for the discussion of future AI. Despite the excitement, the success is still limited to specific types of tasks. Particularly, ChatGPT and similar foundation models have unique characteristics that are difficult to replicate for most geospatial tasks. This paper envisions several major challenges and opportunities in the creation of geospatial foundation (geo-foundation) models, as well as potential future adoption scenarios. We also expect that a major success story is necessary for geo-foundation models to take off in the long term.

GeoDS Students Awards in Spring 2023

Please join us in congratulating our GeoDS lab’s PhD students and undergraduate students’ recent awards and achievements!

Yuhao Kang:

2023 Waldo-Tobler Young Researcher Award in GIScience, by the Austrian Academy of Sciences (ÖAW) Commission for GIScience to encourage scientific advancement in the disciplines of Geoinformatics and/or Geographic Information Science.

2023 CaGIS PhD Student Scholarship Award and 2023 CaGIS RISING research grant, by U.S. Cartography and Geographic Information Society (CaGIS)

Jake Kruse:

2023 Invited Presentation at the UW-Madison Day at the State Capitol of Wisconsin

Yichen Xin (Undergraduate):

2023 Undergraduate Student Winner of the Peter Gould Best Paper Award, by the AAG Health and Medical Geography Specialty Group (HMGSG)

Wen Ye (Undergraduate):

2023 Undergraduate Fellows Seminar, Hilldale Undergraduate/Faculty Research Fellowships by UW-Madison

Sessions at the 2023 AAG GeoAI Symposium

Sessions at the 2023 AAG GeoAI Symposium (PDF download)

Symposium Lead Organizers:

Yingjie Hu, University at Buffalo

Song Gao, University of Wisconsin, Madison

Wenwen Li, Arizona State University

Dalton Lunga, Oak Ridge National Laboratory

Orhun Aydin, Saint Louis University

Shawn Newsam, University of California, Merced

3/23/2023, Thursday

GeoAI and Deep Learning Symposium: GeoAI for Feature Detection and Recognition 

GeoAI and Deep Learning Symposium: Deploying AI for Geospatial Data and Remote Sensing: Advances, Challenges and Obstacles 

GeoAI and Deep Learning Symposium: A 5-year Milestone: Advances and Limitations in GeoAI Research So Far 

  • Session Link: https://aag.secure-platform.com/aag2023/solicitations/39/sessiongallery/5663
  • Organizers: Yingjie Hu, Song Gao, Wenwen Li, Dalton Lunga, Orhun Aydin, and Shawn Newsame
  • Panelists: Michael Goodchild, University of California Santa Barbara, A-Xing Zhu, University of Wisconsin, Madison, May Yuan, University of Texas Dallas, Orhun Aydin, Saint Louis University, Budhendra Bhaduri, Oak Ridge National Laboratory)
  • Date: 3/23/2023 (Thursday)
  • Time: 12:50 PM – 2:10 PM Mountain Time
  • Room: Capitol Ballroom 1, Hyatt Regency, Fourth Floor

GeoAI and Deep Learning Symposium: Emerging Geo-Data Applications in Human Mobility Analysis 

GeoAI and Deep Learning Symposium: GeoAI for Cartography and Mapping 

3/24/2023, Friday

GeoAI and Deep Learning Symposium: Spatial Data Science for Ecosystem Conservation and Biodiversity

GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence I

GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence II

GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence III

3/25/2023, Saturday

GeoAI and Deep Learning Symposium: Intelligent Geospatial Analytics 

GeoAI and Deep Learning Symposium: GeoAI for Disaster Resilience 

3/26/2023, Sunday

GeoAI and Deep Learning Symposium: Urban Visual Intelligence 

GeoAI and Deep Learning Symposium: Geoprivacy and Ethics in Geospatial Data and GeoAI 

Program Committee:

Sean C. Ahearn, Hunter College — CUNY 

Samantha T. Arundel, US Geological Survey

Orhun Aydin, Saint Louis University

Andrea Ballatore, King’s College London

Budhendra Bhaduri, Oak Ridge National Lab

Ling Bian, University at Buffalo

Kai Cao, East China Normal University

Guofeng Cao, University of Colorado, Boulder

Yao-Yi Chiang, University of Minnesota-Twin Cities

Somayeh Dodge, University of California Santa Barbara

Chen-Chieh Feng, National University of Singapore

Amy Frazier, Arizona State University

Michael F. Goodchild, University of California, Santa Barbara

Majid Hojati, Wilfrid Laurier University

Yingjie Hu, University at Buffalo

Xiao Huang, University of Arkansas

Qunying Huang, University of Wisconsin, Madison

Jamon Van Den Hoek, Oregon State University 

Krzysztof Janowicz, University of Vienna & University of California, Santa Barbara

Chaogui Kang, China University of Geosciences

Yuhao Kang, University of Wisconsin, Madison

Carsten Keßler, Aalborg University Copenhagen

Morteza Karimzadeh, University of Colorado Boulder

Jina Kim, University of Minnesota

Junghwan Kim, Virginia Tech

Nina Lam, Louisiana State University

Xiaojiang Li, Temple University

Wenwen Li, Arizona State University

Xiao Li, University of Oxford

Yu Liu, Peking University, China

Tao Liu, Michigan Technological University

Grant McKenzie, McGill University

Antonio Medrano, Texas A&M University Corpus Christi

Gengchen Mai, University of Georgia

Yi Qiang, University of South Florida

Alex Sorokine, Oak Ridge National Lab

Avipsa Roy, University of California, Irvine

Kathleen Stewart, University of Maryland, College Park

Marcela Suárez, Penn State University

Wenwu Tang, University of North Carolina at Charlotte

Daoqin Tong, Arizona State University

Ming-Hsiang Tsou, San Diego State University

Mingshu Wang, University of Glasgow

Shaohua Wang, Chinese Academy of Sciences

Shaowen Wang, University of Illinois, Urbana-Champaign

Zhangyu Wang, University of California Santa Barbara

Dawn Wright, Esri Inc.

Hsiuhan (Lexie) Yang, Oak Ridge National Laboratory

Haowen Xu, Oak Ridge National Laboratory

Angela Yao, University of Georgia

Xinyue Ye, Texas A&M University

Eun-Hye Enki Yoo, University at Buffalo

Manzhu Yu, Penn State University

May Yuan, University of Texas at Dallas

Fan Zhang, Hong Kong University of Science and Technology

Hongyu Zhang, McGill University

Bo Zhao, University of Washington

A-Xing Zhu, University of Wisconsin, Madison

Di Zhu, University of Minnesota, Twin Cities

Lei Zou, Texas A&M University

This symposium is sponsored by: AAG GISS, CI and SAM specialty groups

Geospatial Data Science Speaker Series Spring 2023

Dear colleagues and students, 

Greetings!  I am very glad to invite you to mark your calendar for joining the forthcoming Geospatial Data Science Speaker Series Spring 2023 events, which are hosted by the GeoDS lab in Geography and co-sponsored by the Data Science Institute, UniverCity Alliance, and GISPP @UW-Madison.  We will have Dr. Filip Biljecki, the Director of Urban Analytics Lab from the National University of Singapore visit UW-Madison 11:45 a.m.-1 p.m., on March 28, 2023 (Tue), Science Hall 110 and Dr. Fabio Duarte from the MIT Senseable City Lab on April 13 (Thur), Science Hall 140. Pizza lunch and coffee will be provided in the events. 

Prof. Gao joins the Associate Editors team of IJGIS

Recently, Prof. Song Gao was invited to join the Associate Editors team of International Journal of Geographical Information Science (IJGIS), which is a flagship international journal for publishing geographic information systems/science related research. Dr. Gao’s service term starts from January 1st, 2023.

Aims and Scope

The aim of International Journal of Geographical Information Science is to provide a forum for the exchange of original ideas, approaches, methods and experiences in the field of GIScience.

International Journal of Geographical Information Science covers the following topics:

  • Innovations and novel applications of GIScience in natural resources, social systems and the built environment
  • Relevant developments in computer science, cartography, surveying, geography, and engineering
  • Fundamental and computational issues of geographic information
  • The design, implementation and use of geographical information for monitoring, prediction and decision making

Prof. Song Gao was named the Highly Cited Researcher

Prof. Song Gao is on this year’s list of Global Highly Cited Researchers List of 2022 and the only scholar from UW-Madison listed in the category of Social Sciences. Kudos to his colleagues, students, and mentors!

On November 15 2022, Clarivate revealed its 2022 list of Highly Cited Researchers™ – individuals at universities, research institutes and commercial organizations who have demonstrated a disproportionate level of significant and broad influence in their field or fields of research. The methodology draws on data from the Web of Science™ citation index, together with analysis performed by bibliometric experts and data scientists at the Institute for Scientific Information (ISI)™ at Clarivate. ISI analysts have awarded Highly Cited Researcher 2022 designations to 6,938 researchers from across the globe who demonstrated significant influence in their chosen field or fields over the last decade. ISI analyzed all papers published and cited between 2011 and 2021, determining which authors ranked in the top 1% of cited papers in each field. The list is truly global, spanning 69 countries or regions and spread across a diverse range of research fields in the sciences and social sciences.

Prof. Gao is also on the list of top 2% highly cited scientists based on Stanford University’s analysis of Scopus data provided by Elsevier.

UW-Madison Research News: https://research.wisc.edu/uncategorized/2022/11/22/uw-madison-faculty-make-strong-showing-on-global-highly-cited-researchers-list/