Author: Song Gao

CFP: GIScience 2025 in New Zealand

Song Gao

The Thirteenth International Conference on Geographic Information Science (GIScience 2025) will be held in Christchurch, New Zealand, on 26-29 August 2025, hosted by the University of Canterbury in collaboration with the GIScience academic research community across New Zealand. GIScience 2025 is the flagship conference in geographic information science and continues the highly successful conference series which started in 2000.

The conference regularly attracts over 250 international participants from academia, industry, and government to discuss and advance the state-of-the-art in geographic information science. August 26 is dedicated to Workshops and Tutorials. The main conference runs from August 27 to 29 and includes a single refereed paper track and an abstract track for posters and demo submissions. GIScience research spans the gamut of interrelated discovery activities related to geographic information from the invention of new computational instruments, the gathering of data via observation or experimentation, and discovery of descriptive generalizations patterns in data through to the creation of explanatory theories and the testing of theories. The GIScience conference series seeks submissions that make fundamental advances to the field through this multifaceted process.

GIScience 2025 welcomes papers, posters and demos covering emerging topics and fundamental research findings across all sectors of geographic information science, including (but not limited to) the role of geographic information in geography, computer science, engineering, information science, linguistics, mathematics, cognitive science, philosophy, psychology, social science, and geostatistics. We welcome participation from community members sharing work at various stages of development, including position pieces, works in progress, as well as full papers for inclusion in the conference proceedings.

Proceedings papers Deadline: January 31, 2025

Abstracts and Demos Deadline: April 4, 2025

Conference Website: https://giscience2025.org

CFP: 2025 AAG Symposium on GeoAI and Deep Learning for Geospatial Research

Song Gao

CFP: 2025 AAG Symposium on GeoAI and Deep Learning for Geospatial Research

2025 AAG Annual Meeting, Detroit, MI, March 24-28, 2025

https://bit.ly/aag2025geoai

Lead Organizers:

Song Gao, University of Wisconsin, Madison

Wenwen Li, Arizona State University

Yingjie Hu, University at Buffalo

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 Texas Austin

Krzysztof Janowicz, University of Vienna

The field of GeoAI is advancing at an astonishing speed. We are excited to witness the significant growth of GeoAI in terms of its methods, its diverse geospatial applications, and its increasing societal impacts. For example, GeoAI has been applied to advance our understanding of environmental and climate change on the earth, improve individual and population health, enhance community resilience in natural disasters, strengthen smart and connected communities, more accurately predict spatiotemporal traffic flows, support humanitarian mapping and policymaking, and more. From the perspective of methodological development, we have observed a paradigm shift from using task-specific models with supervised learning to leveraging the power of visual foundation models, large language models (LLMs), and multimodal foundation models to achieve zero-shot to few-shot geospatial learning. We have also seen an increasing body of pioneering research integrating spatial theories and principles into general AI model design to develop “spatialized” AI that best tackles spatial and spatiotemporal problems.

Building on the success of previous AAG GeoAI symposiums and continuing to push the cutting edge of GeoAI research and its societal impact, the 2025 symposium aims to bring together geographers, GI scientists, remote sensing scientists, computer scientists, health researchers, urban planners, transportation professionals, disaster response experts, ecologists, earth system scientists, stakeholders, and many others to share recent research outcomes and discuss challenges for GeoAI research in the following years.

Sessions (all sessions will be accessed at: https://bit.ly/aag2025geoai):

  • GeoAI and Deep Learning Symposium: Representing Geographic Locations in AI Models (Panel discussion session; In-person session; Contact: Yingjie Hu, yhu42@buffalo.edu, University at Buffalo; Panelists: Gengchen Mai, University of Texas at Austin; Morteza Karimzadeh, University of Colorado Boulder; Yiqun Xie, University of Maryland College Park, Ziqi Li, Florida State University)
  • GeoAI and Deep Learning Symposium: Generative AI in GIScience: a research agenda towards autonomous GIS (Panel discussion session; In-person session; Contact: Zhenlong Li,zhenlong@psu.edu, Penn State University)
  • GeoAI and Deep Learning Symposium: GeoAI for Sustainability & Resilience I: Research Advances (Panel discussion session; in-person session; The organizing team)
  • GeoAI and Deep Learning Symposium: GeoAI for Sustainability & Resilience II:: From Research to Real-World Applications (Panel discussion session; in-person session; The organizing team)
  • GeoAI and Deep Learning Symposium: Current Developments and Concerns in GeoAI and CartoAI (Panel discussion session; In-person session; Contact:  Aileen Buckley, Esri (abuckley@esri.com); Sam Arundel, sarundel@usgs.gov, U.S. Geological Survey)
  • GeoAI and Deep Learning Symposium: GeoAI for Feature Detection and Recognition (Paper session; In-person session; Contact: Sam Arundel, sarundel@usgs.gov, U.S. Geological Survey; Co-organizers: Wenwen Li, Arizona State University; Kevin McKeehan, HNTB, and Ernie Liu, U.S. Geological Survey )
  • GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence (Paper session; In-person session; Contact: Gengchen Mai, gengchen.mai@austin.utexas.edu, University of Texas at Austin; Co-organizers:Yao-Yi Chiang, University of Minnesota-Twin Cities; Zhangyu Wang, University of California Santa Barbara; Di Zhu, University of Minnesota-Twin Cities; Yiqun Xie, University of Maryland; Hao Yang, University of Georgia)
  • GeoAI and Deep Learning Symposium: GeoAI for Spatial Analytics and Modeling (Paper session; In-person session; Contact: Di Zhu, dizhu@umn.edu, University of Minnesota; Co-organizers: Guofeng Cao, University of Colorado, Boulder; Song Gao, University of Wisconsin, Madison; Peng Luo, Massachusetts Institute of Technology)
  • GeoAI and Deep Learning Symposium: UrbanAI for Sustainable, Climate-Resilient Environments (Paper session; In-person session; Contact: Steffen Knoblauch, steffen.knoblauch@uni-heidelberg.de, Heidelberg University; Co-organizers: Hao Li, Technical University Munich; Peng Luo, Massachusetts Institute of Technology; Filip Biljecki, National University of Singapore; Alexander Zipf, Heidelberg University)
  • GeoAI and Deep Learning Symposium: GeoAI and Urban Mobility Analytics (Paper session; In-person session; Contact: Dan Qiang, dan.qiang@mail.mcgill.ca, McGill University; Co-organizers: Grant McKenzie, McGill University, Xiao Huang, Emory University; Yihong Yuan, Texas State University)
  • GeoAI and Deep Learning Symposium: GeoAI for Disaster Resilience (Paper session; In-person session; Contact: Bing Zhou, bbz5159@psu.edu, Penn State University. Co-organizers: Zhenqi (Ryan) Zhou, University at Buffalo; Lei Zou, Texas A&M University; Yingjie Hu, University at Buffalo; Qunying Huang, University of Wisconsin-Madison; 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: GeoAI for Human Geography (Paper session; In-person session; Contact: Xiao Huang, Emory University, xiao.huang2@emory.edu; Co-organizers: Siqin Wang, University of Southern California; Peter Kedron, University of California, Santa Barbara; John Wilson, University of Southern California)
  • GeoAI and Deep Learning Symposium: Human-Centered Geospatial Data Science (Paper session; Hybrid session; Contact: Jiaxin Feng, Jiaxin.Feng@dartmouth.edu, Dartmouth College; Co-organizers: Hoeyun Kwon, Lehman College – The City University of New York; Yuhao Kang, UT Austin)
  • GeoAI and Deep Learning Symposium: Social Sensing and GeoAI for Public Health (Paper session; In-person session; Contact: Binbin Lin, bb2020@tamu.edu, Texas A&MUniversity; Co-organizers: Lei Zou, Texas A&M; Yi Qiang, University of South Florida; Xiao Huang, Emory University)
  • GeoAI and Deep Learning Symposium: Generative AI – Opportunities and Challenges for GIScience Research (Paper session; in-person session; Contact: Junghwan Kim, Virginia Tech, junghwankim@vt.edu Co-organizer: Kee Moon Jang, MIT; Jinhyung Lee, Western University; Yuhao Kang, UT Austin)
  • GeoAI and Deep Learning Symposium: Spatial Data Science and GeoAI Ethics: Bridging Research and Education (Paper session; Hybrid session; Contact: Yue Lin, liny2@uchicago.edu, University of Chicago; Co-organizers: Hongyu Zhang, University of Massachusetts Amherst; Bing Zhou, Penn State University)
  • GeoAI and Deep Learning Symposium: GeoAI for Cartography (Paper session; Contact: Xiran Zhou, China University of Mining and Technology, xrzhou@cumt.edu.cn), Wenwen Li, Arizona State University)

To present your research in one of these sessions, please register and submit your abstract at https://aag.secure-platform.com/aag2025/. When you receive confirmation of your submission, please forward your confirmation email to the session organizers by November 14, 2024.

Geospatial Data Science Seminar by Professor Kevin Mwenda at Brown University

Song Gao

We are very glad to invite you to mark your calendar for joining the forthcoming Geospatial Data Science Speaker Series 2024-2025 events, which are hosted by the GeoDS lab in Geography and co-sponsored by the Data Science Institute @UW-Madison. 

The first event of this semester will be jointly with the Geography Yi-Fu Lectures. We will first have Dr. Kevin Mwenda, an Associate Professor of Population Studies (Research) at the Population Studies and Training Center (PSTC) and the Director of the Spatial Structures in the Social Sciences (S4), visiting UW-Madison and will present “Beyond Maps: Integrating Place and Space for Community Resilience” at 3:30 p.m.-4:30p.m., on October 18, 2024 (Friday), Science Hall 180.

Geospatial Data Science Seminar by Professor Krzysztof Janowicz

Song Gao

We are very glad to invite you to mark your calendar for joining the forthcoming Geospatial Data Science Speaker Series 2024-2025 events, which are hosted by the GeoDS lab in Geography and co-sponsored by the Data Science Institute @UW-Madison. 

The first event of this semester will be jointly with the Geography Yi-Fu Lectures. We will first have Dr. Krzysztof Janowicz, a distinguished University-Named Professor of Geoinformatics at the University of Vienna (Austria), visiting UW-Madison and will present “GeoMachina: What Designing Artificial GIS Analysts Teaches Us About Place Representation” at 3:30 p.m.-4:30p.m., on September 13, 2024 (Friday), Science Hall 180.

Ying Nie received the 2024 University HILLDALE FELLOWSHIP

Song Gao

Please join us congratulating our senior student Ying Nie, who is currently an undergraduate majoring in computer science as well as a research assistant in the GeoDS Lab under Prof. Song Gao’s mentorship, just got the UW-Madison “Hilldale Undergraduate/Faculty Research Fellowship” and was awarded  in the 2024 Chancellor’s Undergraduate Awards Ceremony! 

The awarded research project is: Large Language Model for Intelligent Spatial Analysis Workflow Construction

.

In 2022, our GeoDS Lab’s alumnus Wendy Ye (who is currently a PhD student at USC Compute Science) also got this university fellowship.

In 2019, our GeoDS Lab’s alumnus Timothy Prestby (who is currently a PhD student at PSU Geography) also got this university fellowship.

Other Previous Hilldale Fellows at the University of Wisconsin-Madison:

https://awards.advising.wisc.edu/campus-wide-award-recipients/test-hilldale-fellows/

GeoDS Lab members and alumni at AAG 2024

Song Gao

Our GeoDS lab’s students and alumni recently attended the American Association of Geographers (AAG) 2024 Annual Meeting held in Honolulu, HI. It was a great reunion for the GeoDS family at the conference!

Here are the sessions we led and participated:

GeoAI and Deep Learning Symposium: GeoAI Foundation Models

Type: Panel Date: 4/16/2024

GeoAI and Deep Learning Symposium: GeoAI for Science and the Science of GeoAI

Type: Panel Date: 4/17/2024

Symposium on Geospatial Data Science for Sustainability: Convergence Curriculum for Geospatial Data Science

Type: Panel Date: 4/17/2024

Symposium on Human Dynamics Research: Human Dynamics meets GeoAI

Type: Panel Date: 4/18/2024

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

Type: Paper Date: 4/16/2024

Presenter: Yuhan Ji

GeoAI and Deep Learning Symposium – Responsible GeoAI I: Privacy and Fairness

Type: Paper Date: 4/18/2024

GeoAI and Deep Learning Symposium – Responsible GeoAI II: Justice and Accuracy

Type: Paper Date: 4/18/2024

Presenter: Qianheng Zhang

GISS-SG Student Honors Paper Competition

Type: Paper Date: 4/18/2024

Presenter: Jake Krue

GeoAI and Deep Learning Symposium: GeoAI for Sustainable and Computational Agriculture I

Type: Paper Date: 4/18/2024

Primary Organizer: Jinmeng Rao, Google DeepMind

Symposium on GeoAI and Deep Learning for Geospatial Research: Human-centered Geospatial Data Science

Type: Paper Date: 4/19/2024

Primary Organizer: Yuhao Kang, University of South Carolina

GeoAI and Deep Learning Symposium: Emerging Geo-Big Data Applications in Human Mobility Analysis I: Transport & Social Challenges

Type: Paper Date: 4/19/2024

Presenter: Yichen Xu

 GeoAI and Deep Learning Symposium: GeoAI for Spatial Analytics and Modeling

Type: Paper Date: 4/20/2024

Presenter: Yunlei Liang

ITU AI for Good Webinar on GeoAI Solutions for Sustainable Development

Song Gao

The International Telecommunication Union (ITU) of the United Nations is organizing the webinar series on AI for Good. You are cordially invited to join the forthcoming one is about GeoAI Discovery. 

Topic: GeoAI Solutions for Sustainable Development: The Handbook of Geospatial Artificial Intelligence (GeoAI)

Date and Time: 23 February 2024, Friday, at 16:00 CET Geneva | 10:00-11:00 EST, New York | 23:00-00:00 CST, Beijing

Recording link: https://www.youtube.com/live/QHSl4uvioMk?feature=shared

Geospatial Artificial Intelligence (GeoAI) is a rapidly evolving interdisciplinary field that integrates geospatial studies with AI advancements. In this webinar editors and authors of the recently published GeoAI Handbook discuss the fundamental concepts, methods, applications, and perspectives of GeoAI. The GeoAI Handbook is an excellent resource for educators, students, practitioners and decision-makers who are interested in utilizing AI technologies in a geospatial context. 

Schedule:

20 mins: Round-table Q&A about the GeoAI Handbook:  Maria Antonia Brovelli, Andrea Manara, and Song Gao

10 mins: Chapter 5: GeoAI for Spatial Image Processing:  Wenwen Li and Samantha Arundel

10 mins: Chapter 7: Intelligent Spatial Prediction and Interpolation Methods: Di Zhu

10 mins: Chapter 10: Spatial Cross-Validation for GeoAI: Yingjie Hu 

10 mins: Wrap-up 

The GeoAI advancements provide promising solutions to address some of the United Nations SDGs but also pose concerns. For example, Chapter 3 presents some of the fundamental assumptions and principles that could form the philosophical foundation of GeoAI and spatial data science. It highlights the sustainability issue for training GeoAI and foundation models that could cause substantial electricity energy and resource consumptions and generate equivalent carbon emissions. Therefore, we need to call for Green AI for achieving the SDG-13: Climate Action. Chapters 13 and 14 discuss existing and prospective GeoAI tools to support humanitarian assistance practices and disaster responses using geospatial big data and machine learning methods, aiming to address the SDG-10: Reduce Inequality and SDG-11: Sustainable Cities and Communities. Chapter 15 focuses on using GeoAI for infectious disease spread prediction to address the SDG-3: Good Health and Well-Being.

AI technologies are advancing rapidly, and new methods and use cases in GeoAI are constantly emerging.  As GeoAI researchers, we should not purely hunt for latest AI technologies but should focus on addressing geographic problems and solving grand challenges facing our society as well as achieving sustainable development goals. We also need research effort toward the development of responsible, unbiased, explainable and interpretable GeoAI models to support geographic knowledge discovery and beyond. This GeoAI Handbook was completed in the middle of 2023. While it cannot summarize all GeoAI research in this one handbook, it provides a snapshot of current GeoAI research landscape and helps stimulate future studies in the coming years.

Geospatial Data Science Speaker Series Spring 2024

Song Gao

Greetings!  We are very glad to invite you to mark your calendar for joining the forthcoming Geospatial Data Science Speaker Series Spring 2024 events, which are hosted by the GeoDS lab in Geography and co-sponsored by the Data Science Institute @UW-Madison. 

We will first have Dr. Amr Magdy, an Assistant Professor of Computer Science and Engineering and a co-founding faculty member of the Center for Geospatial Sciences at UC Riverside, visiting UW-Madison and will present “Scalable Spatial Data Science for Social Scientists” 12:00 p.m.-1 p.m., on February 13, 2024 (Tue), Science Hall 140. Pizza lunch and coffee will be provided in the events. 

New GeoAI Handbook published

Song Gao

The new “Handbook of Geospatial Artificial Intelligence” edited By Drs. Song Gao (University of Wisconsin-Madison), Yingjie Hu (University at Buffalo), and Wenwen Li (Arizona State University) is now published! Dr. Michael F. Goodchild (University of California-Santa Barbara) wrote a Foreword to provide a historic context and recent advances to help the reader to understand the significant shift in the geographic sciences with AI.

This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI technologies such as machine (deep) learning and knowledge graph. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, the GeoAI handbook is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, geography, and beyond!

Book chapters and their authors:

Section 1: Historical Roots of GeoAI

Chapter 1: Introduction to Geospatial Artificial Intelligence (GeoAI)

By Song Gao, Yingjie Hu, Wenwen Li

Chapter 2: GeoAI’s Thousand-Year History

By Helen Couclelis

Chapter 3: Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science

By Krzysztof Janowicz

Section 2: GeoAI Methods

Chapter 4: GeoAI Methodological Foundations: Deep Neural Networks and Knowledge Graphs

By Song Gao, Jinmeng Rao, Yunlei Liang, Yuhao Kang, Jiawei Zhu, Rui Zhu 

Chapter 5: GeoAI for Spatial Image Processing

By Samantha T. Arundel, Kevin G. McKeehan, Wenwen Li, Zhining Gu

Chapter 6: Spatial Representation Learning in GeoAI

By Gengchen Mai, Ziyuan Li, Ni Lao

Chapter 7: Intelligent Spatial Prediction and Interpolation Methods

By Di Zhu, Guofeng Cao

Chapter 8: Heterogeneity-Aware Deep Learning in Space: Performance and Fairness

By Yiqun Xie, Xiaowei Jia, Weiye Chen, Erhu He

Chapter 9: Explainability in GeoAI

By Ximeng Cheng, Marc Vischer, Zachary Schellin, Leila Arras, Monique M. Kuglitsch, Wojciech Samek, Jackie Ma

Chapter 10: Spatial Cross-Validation for GeoAI

By Kai Sun, Yingjie Hu, Gaurish Lakhanpal, Ryan Zhenqi Zhou

Section 3: GeoAI Applications

Chapter 11: GeoAI for the Digitization of Historical Maps

By Yao-Yi Chiang, Muhao Chen, Weiwei Duan, Jina Kim, Craig A. Knoblock, Stefan Leyk, Zekun Li, Yijun Lin, Min Namgung, Basel Shbita, Johannes H. Uhl 

Chapter 12: Spatiotemporal AI for Transportation

By Tao Cheng, James Haworth, Mustafa Can Ozkan 

Chapter 13: GeoAI for Humanitarian Assistance

By Philipe A. Dias, Thomaz Kobayashi-Carvalhaes, Sarah Walters, Tyler Frazier, Carson Woody, Sreelekha Guggilam, Daniel Adams, Abhishek Potnis, Dalton Lunga 

Chapter 14: GeoAI for Disaster Response

By Lei Zou, Ali Mostafavi, Bing Zhou, Binbin Lin, Debayan Mandal, Mingzheng Yang, Joynal Abedin, Heng Cai 

Chapter 15: GeoAI for Public Health

By Andreas Züfle, Taylor Anderson, Hamdi Kavak, Dieter Pfoser, Joon-Seok Kim, Amira Roess 

Chapter 16: GeoAI for Agriculture

By Chishan Zhang, Chunyuan Diao, Tianci Guo 

Chapter 17: GeoAI for Urban Sensing

By Filip Biljecki 

Section 4: Perspectives for the Future of GeoAI

Chapter 18: Reproducibility and Replicability in GeoAI

By Peter Kedron, Tyler D. Hoffman, Sarah Bardin

Chapter 19: Privacy and Ethics in GeoAI

By Grant McKenzie, Hongyu Zhang, Sébastien Gambs

Chapter 20: A Humanistic Future of GeoAI

By Bo Zhao, Jiaxin Feng

Chapter 21: Fast Forward from Data to Insight: (Geographic) Knowledge Graphs and Their Applications

By Krzysztof Janowicz, Kitty Currier, Cogan Shimizu, Rui Zhu, Meilin Shi, Colby K. Fisher, Dean Rehberger, Pascal Hitzler, Zilong Liu, Shirly Stephen 

Chapter 22: Forward Thinking on GeoAI

By Shawn Newsam

AAG 2024 GeoAI Symposium

Song Gao

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; Chaogui Kang, China University of GeoSciences)
  • 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)
  • GeoAI and Deep Learning Symposium: AI for Earth Observation (Paper session; In-person session; Contact: Bo Peng, PAII, Ping An U.S. Research Lab ; Co-organizer: Chenxi Lin, PAII, Ping An U.S. Research Lab ; Beth Tellman, University of Arizona; Bandana Kar, U.S. Department of Energy; Lexie Yang, Oak Ridge National Laboratory; Yanghui Kang, University of California, Berkeley; Qunying Huang, University of Wisconsin-Madison; Di Zhu, University of Minnesota, Twin Cities)
  • GeoAI and Deep Learning Symposium: Characterization of Place and Human Patterns of Life (Paper session; In-person session; Contact: Junchuan Fan,Oak Ridge National Laboratory; Co-organizer: Joon-Seok Kim, Oak Ridge National Laboratory; Licia Amichi, Oak Ridge National Laboratory)

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

Song Gao

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 Lab received a new NSF grant on IHBEM

Song Gao

Recently, Prof. Song Gao, as a Co-PI, received a collaborative NSF grant from the program of Incorporating Human Behavior in Epidemiological Models (IHBEM), titled ” The fear of here: Integrating place-based travel behavior and detection into novel infectious disease models ”.

Nick Ruktanonchai (Principal Investigator) ,Virginia Tech

Shengjie Lai (Co-PI) , University of Southampton

Omar Saucedo (Co-PI) , Virginia Tech

Corrine Ruktanonchai (Co-PI) , Virginia Tech

Song Gao (Co-PI), University of Wisconsin-Madison

Robert Holt (Co-PI) , University of Florida

Nicholas Kortessis (Co-PI), Wake Forest University

ABSTRACT

When people change where, when, and why they travel, there are effects on infectious diseases. People?s movements determine who is at risk of the disease and whether new cases are counted by local public health agencies. For example, during the COVID-19 pandemic, people?s movements changed drastically and, in addition to COVID-19, influenza and Lyme disease cases also dropped nationwide. These drops in cases may be because people spent less time in high risk areas, or simply because people traveled to healthcare facilities less frequently, and so fewer cases are reported. Distinguishing between these alternatives is critical for understanding disease control and predicting disease spread, but is made difficult when travel patterns change dramatically. This problem is especially challenging because communities may modify travel patterns in response to local disease, which can, in turn, change how diseases spread in communities and how public health monitors disease. To determine the cause of case reductions as human movements changed, the Investigators will develop new mathematical models that account for the ways travel impacts both risk and detection, using data from mobile phones to inform transmission risk and using local surveys to inform underdetection rates. By developing this new collection of models, the Investigators will better understand how transmission and detection of various non-COVID-19 infections changed throughout the pandemic, recognize how this depends on the biology of the disease being considered, and predict how case numbers may change during future periods of significant community-level changes in travel.

Community-level travel patterns have multifactorial effects on the dynamics of any infectious disease. Major changes to travel patterns affect both transmission, as people spend more or less time in high-risk places, and detection, as people change their propensity to visit healthcare facilities. These factors also influence individual behaviour, because local increases in reported cases can cause people to change their travel further. This creates critically important feedback loops between transmission, detection, and travel. Depending on the interactions between these factors, changes to travel or transmission could lead to undercounting of cases or a harmful population-level response that leads to communities being exposed to more infections. As changes in community-level travel patterns become more likely with global factors such as climate change and emerging infectious disease threats, it becomes increasingly important for models to integrate their effects on both detection and transmission. The project addresses this need by developing novel models that account for the ways in which travel can simultaneously affect both transmission and detection, and be affected by reported and perceived disease risk. The Investigators will combine the models with mobility data obtained from SafeGraph and use local surveys to inform underdetection rates of key notifiable diseases across the New River Valley Health District of Virginia, and to develop a framework for predicting transmission and detection changes during future large-scale changes in travel. Central Appalachia is a key region for this work, as it experiences relatively high incidence of respiratory and Lyme diseases, and intervention adherence was especially low during the later stages of the COVID-19 pandemic.

This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).

Two papers accepted at GIScience 2023

Song Gao

The 12th International Conference on Geographic Information Science (GIScience 2023), which is a flagship conference in the field of GIScience, will be held 12 – 15th September, 2023. Leeds, UK. GeoDS lab members have two papers accepted as oral presentations.

Yuhan Ji, Song Gao (2023) Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations. In the Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), No. 43; pp. 43:1–43:6

Qianheng Zhang, Yuhao Kang, Robert Roth (2023) The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography. In the Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), No. 93; pp. 93:1–93:6

GeoDS Students Awards in Spring 2023

Song Gao

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

Geospatial Data Science Speaker Series Spring 2023

Song Gao

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

Song Gao

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

Song Gao

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/

GeoDS lab members in the 2022 ACM SIGSPATIAL and AutoCarto Conferences

Song Gao

During the week of November 1-4, 2022, all the GeoDS lab members were traveling to two academic conferences: ACM SIGSPATIAL 2022 and AutoCarto 2022.

Prof. Song Gao, Wen Ye (undergraduate student), Yunlei Liang (PhD student), Yuhan Ji (PhD student), Jiawei Zhu (visiting PhD student), and Jinmeng Rao (PhD Candidate), presented at the 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022) in Seattle, Washington, USA.

We published two short research papers in the main conference, three workshop full papers, and won a “Best Paper Award”.

  1. Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions. Yunlei Liang, Jiawei Zhu, Wen Ye, Song Gao. (2022) In SIGSPATIAL’22, DOI:10.1145/3557915.3560974
  2. Exploring multilevel regularity in human mobility patterns using a feature engineering approach: A case study in Chicago. Yuhan Ji, Song Gao, Jacob Kruse, Tam Huynh, James Triveri, Chris Scheele, Collin Bennett, and Yichen Wen. (2022) In SIGSPATIAL’22, DOI:10.1145/3557915.3560974
  3. (Best Paper Award) Understanding the spatiotemporal heterogeneities in the associations between COVID-19 infections and both human mobility and close contacts in the United States. Wen Ye, Song Gao. (2022) In SpatialEpi ’22,  pp 1–9, DOI:10.1145/3557995.3566117
  4. Measuring network resilience via geospatial knowledge graph: a case study of the us multi-commodity flow network. Jinmeng Rao, Song Gao, Michelle Miller, Alfonso Morales. (2022) In GeoKG’22, pp 17-25, DOI:10.1145/3557990.3567569
  5. Towards the intelligent era of spatial analysis and modeling. Di Zhu, Song Gao, Guofeng Cao. (2022) In GeoAI’22, pp 10-13, DOI:10.1145/3557918.3565863

As the Proceedings Chairs, Professor Song Gao co-organized the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI’22). There are two keynotes from both industry and academia and 12 oral presentations in the GeoAI workshop. The proceedings of the GeoAI’22 workshop is available at the ACM Digital Library: https://dl.acm.org/doi/proceedings/10.1145/3557918

In addition, Yuhao Kang (PhD Candidate) and Jake Kruse (PhD Student) presented two short papers in the AutoCarto 2022,  the 24th International Research Symposium on Cartography and GIScience.

  1. A Review and Synthesis of Recent GeoAI Research for Cartography: Methods, Applications, and Ethics. Yuhao Kang, Song Gao, Robert Roth (2022)
  2. Interactive Web Mapping for Multi-Criteria Assessment of Redistricting Plans. Jacob Kruse, Song Gao, Yuhan Ji and Kenneth Mayer (2022)

Also, Congrats to Yuhao who won the International Cartographic Association (ICA) Scholarship Award!

Wen Ye received the 2022 University HILLDALE FELLOWSHIP

Song Gao

Please join us congratulating our junior student Wen (Wendy) Ye, who is currently an undergraduate triple-majoring in computer science, data science, and statistics as well as a research assistant in the GeoDS Lab under Prof. Song Gao’s mentorship, just got the UW-Madison “Hilldale Undergraduate/Faculty Research Fellowship” and will be awarded  in the 2022 Chancellor’s Undergraduate Awards Ceremony! 

The awarded research project is: Understanding spatial inequality to health care access in Wisconsin through deep learning-based network analysis.

In 2019, our GeoDS Lab’s alumnus Timothy Prestby (who is currently a PhD student at PSU Geography) also got this university fellowship under Prof. Gao’s mentorship.

Other Previous Hilldale Fellows at the University of Wisconsin-Madison:

https://awards.advising.wisc.edu/campus-wide-award-recipients/test-hilldale-fellows/

Prof. Gao joins the Editorial Board of Transactions in GIS

Song Gao

Recently, Prof. Song Gao is invited to join the Editorial Board of Transactions in GIS, which is a key international journal for publishing geographic information systems/science related research.

Aims and Scope

Transactions in GIS is an international, peer-reviewed journal that publishes original research articles, review articles, and short technical notes on the latest advances and best practices in the spatial sciences. The spatial sciences include all of the different ways in which geography may be used to organize, represent, store, analyze, model and visualize information. The submission of manuscripts that focus on one or more of the following topics among others – is strongly encouraged:

  • GIS, GPS, Remote Sensing and related geospatial technologies;
  • geospatial data acquisition and sensing; maps and spatial reasoning;
  • spatial data infrastructures; standardization and interoperability;
  • spatial data structures and databases; geocomputation;
  • spatiotemporal analysis, integration and modeling;
  • spatial data quality and uncertainty;
  • GIS education and certification; GIS and society;
  • location privacy;
  • and desktop, mobile and Web-based spatially-enabled applications and services.

Keywords

Geographic Knowledge Discovery and Data Mining; Geographic Information Retrieval; Geosensor Networks; Geosimulation; Geospatial Data Integration; Geospatial Semantic Web; Geovisualization; Geographic Information Science; Geographic Information Systems; GIS Architectures and Middleware; GIS and Society; GIS Standardization and Interoperability; GIS&T Education; Global Positioning Systems; Local, Enterprise, Mobile and Web Applications; Location-Based Services; Location Privacy, Data Sharing and Security Maps and Map Services; Ontologies and taxonomies; Public Participation; GIS Remote Sensing; Spatial Analysis; Spatial Cognition and Reasoning; Spatial Data Infrastructures; Spatial Data Quality and Uncertainty; Spatial Databases, Data Structures and Algorithms; Spatial Decision Support Systems; Spatial Dynamics; Spatial Modeling; Spatial Networks; Spatial Thinking; Spatiotemporal Analysis and Modeling; The Spatial Sciences