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. 

Prof. Song Gao was named the 2024 AAG Fellow

Jay Scholz

The American Association of Geographers (AAG) Fellows is a recognition and service program that applauds geographers who have made significant contributions to advancing geography.  Congratulations to Professor Song Gao who was recently selected into the 2024 AAG Fellows! 

Source: https://www.aag.org/2024-aag-awards-recognition/#fellows

Dr. Song Gao is an associate professor of geography and the director of the Geospatial Data Science Lab at the University of Wisconsin Madison. He has established himself as one of the thought leaders and highly cited scholars in the field of geospatial artificial intelligence (GeoAI) and was heavily involved in the geospatial modeling of the spread of COVID-19. He has successfully mentored young scholars and students in GIScience, offered workshops and webinars for the AAG and other organizations, and is an associate editor for AAG’s International Encyclopedia of Geography and International Journal of Geographical Information Science. Dr. Gao’s involvement with cutting-edge data science and AI techniques, his commitment to taking on and solving important challenges, and his enthusiasm for working with different international organizations make him a strong asset to the AAG.

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

Prof. Gao received the 2023 AAG SAM Emerging Scholar Award

Jay Scholz

Recently, Prof. Song Gao received the 2023 Emerging Scholar Award by the American Association of Geographers (AAG) Spatial Analysis and Modeling (SAM) Specialty Group.

The AAG SAM Emerging Scholar Award The emerging scholar award honors early- to mid-career scholars who have made significant contributions to education and research initiatives that are congruent with the mission of AAG-SAM. The candidates must have received their Ph.D. within the last 10 years and must be a member of the AAG-SAM at the time that the person is being nominated.

Sessions at the 2023 AAG GeoAI Symposium

jinmeng

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

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!

Data Science Fellowship Program Experience

Yunlei Liang

This summer, Yunlei, a Ph.D. student from the GeoDS lab, was selected for a Data Science Fellowship program provided by Correlation One. It is a unique fellowship program designed for Ph.D./Master’s students looking to transition from academia into data science roles with a 5% acceptance rate of over 6,000 global applicants. Here she shares her experience with this valuable educational opportunity:

Through the 7-week program, I was involved in various formats of Data Science learning. Every Saturday, I attended online lectures built on real-world cases and delivered by lecturers from universities like Carnegie Mellon, Duke, & Columbia. We learned skills such as exploratory data analysis (EDA), SQL basics, and statistical testing. I also had the opportunity to connect with an assigned mentor from the industry, and career success coaches to improve professional development. Most importantly, I collaborated with a team of 6 with various backgrounds to develop a capstone project using machine learning and data science skills we learned through the lectures.

I greatly appreciate this opportunity to learn from people who are passionate and enthusiastic about data and to expand my network. If you are interested, here is the link to the program: https://www.correlation-one.com/data-science-for-all-women

Prof. Gao received the 2022 UCGIS Early/Mid-Career Research Award

Jay Scholz

The University Consortium for Geographic Information Science (UCGIS) is pleased to announce that Dr. Song Gao, Associate Professor of Geography, University of Wisconsin – Madison, has been selected to receive its inaugural Early-Mid Career Research Award.

As a young scholar in the field of GIScience, Dr. Gao’s scholarly output constitutes an impressive list of well-cited publications that are proving to furnish innovative ideas and methods impacting the theory and practice of GIScience within the interface of geospatial artificial intelligence (particularly machine learning), big spatial data, and a more humanistic oriented place-based GIS. In addition, Dr. Gao has secured substantial sums of external funding to support his research, and has begun filling GIScience Community leadership roles. The UCGIS Research Awards Review Committee assesses that Dr. Gao has achieved a national and international GIScience profile and reputation that far exceeds expectations for a junior scholar.

The UCGIS Early-Mid Career Research Award is to celebrate an outstanding early-mid career research record of innovative ideas or methods that lead to research impacts on the theory and/or practice of GIScience or geographic information technology.

UCGIS will honor Song Gao and other award recipients as part of its Symposium 2022 programming activities.

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

GeoDS members in 2022 AAG Annual Meeting

Song Gao

Here are a set of sessions in which the GeoDS members will make presentations during the American Association of Geographers (AAG) 2022 Annual Meeting. The time is in the US East Time Zone.

Why geoprivacy matters:An international perspective

Day: 2/26/2022
Start Time: 5:20 PM
End Time: 6:40 PM

Panelist : Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3154

Teaching and Research with ArcGIS: Best Practices, Challenges and Opportunities

Day: 2/27/2022
Start Time: 11:20 AM
End Time: 12:40 PM

Panelist: Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/5101

Is Artificial Intelligence good for Geography?

Day: 2/27/2022
Start Time: 2:00 PM
End Time: 3:20 PM

Panelist: Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3749

Geospatial Health Symposium #10: Access and Utilization of Health Care Services 2

Day: 2/27/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Yunlei Liang: Spatially-Constrained Community Detection for Health Professional Shortage Area Delineation with Human Mobility Data 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: CyberGIS-enabled spatial epidemiology

Day: 2/27/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Panelist: Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3391

Role of GIS in planning smart and resilient cities III

Day: 3/1/2022
Start Time: 11:20 AM
End Time: 12:40 PM

Yuhan Ji: A Data-driven Method for Identifying Potential Zones for Airport Shuttle Bus Services 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: UCGIS GeoAI & CyberGIS Research Initiative- GeoAI and CyberGIS for Advancing Spatial Decision Making

Day: 3/1/2022
Start Time: 11:20 AM
End Time: 12:40 PM

Song Gao: University of Wisconsin; Reflections on the Development of Spatially Explicit Methods for GeoAI 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: Urban Visual Intelligence

Day: 3/1/2022
Start Time: 2:00 PM
End Time: 3:20 PM

Jacob Kruse: Places for play: Understanding human perception of playability in cities using street view images and deep learning 

Yuhao Kang: University of Wisconsin-Madison; Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: GeoAI for Social Sensing

Day: 3/1/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Jinmeng Rao: University of Wisconsin – Madison; CATS: Conditional Adversarial Trajectory Simulation for Privacy-Preserved Data Publication