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2026 AAG Annual Meeting, San Francisco, California, March 17-21, 2026

Lead Organizers:

Yingjie Hu, University at Buffalo

Song Gao, University of Wisconsin, Madison

Wenwen Li, Arizona State University

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 rapidly. New AI models, such as vision foundation models, large language models, and multimodal foundation models, provide new possibilities for developing geospatial solutions. Spatial principles, such as Tobler’s First Law, are being incorporated into AI architectures to create spatially explicit models, while explainable GeoAI methods are being explored to improve the interpretability of results. From an application perspective, GeoAI research continues playing positive roles in addressing societal challenges and helping achieve sustainable development goals. Examples include improving individual and population health, enhancing community resilience to disasters, predicting spatiotemporal traffic flows, forecasting climate change impacts on ecosystems, building smart and connected communities and cities, and supporting humanitarian mapping and policymaking. At the same time, the rapid advancement of GeoAI also carries risks, such as the increased opaqueness of large AI models and the environmental costs of training them. How can we continue leveraging GeoAI for making positive impacts while mitigating potential risks? The 2026 AAG GeoAI Symposium aims to bring together geographers, GIScientists, remote sensing scientists, computer scientists, health researchers, urban planners, transportation professionals, disaster response experts, ecologists, earth system scientists, stakeholders, and others to share recent GeoAI research, discuss challenges, and chart the way forward for the coming years.

Sessions at AAG 2026 (the sessions can be accessed at: https://tinyurl.com/333ha64s) 

• GeoAI and Deep Learning Symposium: AI and Machine Learning Applications in Human Mobility Analytics (Paper session; Contact: Pingping Wang, Texas State University (pingpingwang@txstate.edu). Co-organizers: Yihong Yuan, Texas State University, Yi Qiang, University of South Florida, and Somayeh Dodge, University of California Santa Barbara)
• GeoAI and Deep Learning Symposium: Social Sensing and GeoAI for Public Health (Paper session; Contact: Mingzheng Yang, Texas A&M University (ymz2020@tamu.edu). Co-organizers: Xiao Huang, Emory University,  Lei Zou, Texas A&M University)
• GeoAI and Deep Learning Symposium: Advances and Potential Risks in GeoAI Research (Panel session; Contact: Yingjie Hu, University at Buffalo (yhu42@buffalo.edu); Panelists: Kathleen Stewart, University of Maryland College Park; Wenwen Li, Arizona State University; Peter Kedron, University of California Santa Barbara; Song Gao, University of Wisconsin-Madison; Budhu Bhaduri, Oak Ridge National Lab)
• GeoAI and Deep Learning Symposium: Geosimulation and Its Emerging Directions with AI (Paper session; Contact: Jeon-Young Kang, Kyung Hee University (geokang@khu.ac.kr); Co-organizers: Boyu Wang, University at Buffalo; Fuzhen Yin, University of Colorado Colorado Springs)
• GeoAI and Deep Learning Symposium: AI for Earth Observation (Paper session; Contact: Tang Sui, University of Wisconsin-Madison (tsui5@wisc.edu); Co-organizers: Bo Peng, Amazon; Bandana Kar, National Renewable Energy Laboratory; Qunying Huang, University of Wisconsin-Madison; Zhenlong li, Pennsylvania State University)
• GeoAI and Deep Learning Symposium: UrbanAI for Sustainable, Climate-Resilient Environments (Paper session; Contact: Steffen Knoblauch, Heidelberg University (steffen.knoblauch@uni-heidelberg.de) ; Co-organizers: Hao Li, National University of Singapore; Gengchen Mai, The University of Texas at Austin; Yingjie Hu, University at Buffalo; Wenwen Li, Arizona State University)
• GeoAI and Deep Learning Symposium: AI Ethics and Spatial Equity (Paper session; Contact: Hongyu Zhang, University of Massachusetts Amherst (honzhang@umass.edu) ; Co-organizers: Yue Lin, University of Illinois Urbana-Champaign; Bing Zhou, University of Tennessee Knoxville; Yuhao Kang  The University of Texas at Austin)
• GeoAI and Deep Learning Symposium: GeoAI for Disaster Resilience (Paper session; Contact: Bing Zhou, University of Tennessee, Knoxville (bzhou11@tennessee.edu) ; Co-organizers: Lei Zou, Texas A&M University; Yifan Yang, 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 Disaster Resilience II (Paper session; In-person session; Contact: Xiao Chen (xchen414@asu.edu), Arizona State University, Chenyan Lu, Arizona State University; Wenwen Li, Arizona State University)
• GeoAI and Deep Learning Symposium: Spatial Representation Learning from Raster and Vector Data (Panel session; Contact: Steffen Knoblauch, Heidelberg University (steffen.knoblauch@uni-heidelberg.de) ; Panelists: Wenwen Li, Arizona State University; Song Gao, University of Wisconsin-Madison; Gengchen Mai, University of Texas at Austin)
• GeoAI and Deep Learning Symposium: Advancing Sustainable Geospatial Knowledge through Spatial Reasoning and Open Science (Paper session; Contact: Yanan Wu, University of Central Arkansas (ywu@uca.edu) ; Co-organizers: Chengbin Deng, University of Oklahoma; Tao Hu, Oklahoma State University; Yalin Yang  West Virginia University Press)
• GeoAI and Deep Learning Symposium: The Convergence of Generative AI and GIScience: Challenges and Opportunities (Panel session; Contact: Zhenlong Li,  Penn State University (zhenlong@psu.edu);  Huan Ning,  Emory University;  Song Gao,  University of Wisconsin-Madison;  Arif Masrur,  ESRI;  Temitope Akinboyewa,  Penn State University; Ruixiang Liu,  Penn State University; Ali Khosravi Kazazi,  Penn State University; Wenwen Li,  Arizona State University; Jinmeng Rao, Google DeepMind; Budhendra Bhaduri,  Oak Ridge National Laboratory; Samantha T. Arundele, USGS)
• GeoAI and Deep Learning Symposium: The Convergence of Generative AI and GIScience: Research Agenda Towards Autonomous GIS (Panel session; Contact: Zhenlong Li,  Penn State University (zhenlong@psu.edu);  Co-organizers: Huan Ning,  Emory University;   Song Gao,  University of Wisconsin-Madison;  Arif Masrur,  ESRI;  Temitope Akinboyewa,  Penn State University; Ruixiang Liu,  Penn State University; Ali Khosravi Kazazi,  Penn State University; Wenwen Li,  Arizona State University; Jinmeng Rao, Google DeepMind; Budhendra Bhaduri, Oak Ridge National Laboratory; Samantha T. Arundele, USGS)
• GeoAI and Deep Learning Symposium: The Convergence of Generative AI and GIScience: Autonomous AI Agents Development for Geospatial Tasks (Paper session; Contact: Zhenlong Li,  Penn State University (zhenlong@psu.edu);  Co-organizers: Huan Ning,  Emory University;   Song Gao,  University of Wisconsin-Madison;  Arif Masrur,  ESRI;  Temitope Akinboyewa,  Penn State University; Ruixiang Liu,  Penn State University; Ali Khosravi Kazazi,  Penn State University; Wenwen Li,  Arizona State University; Jinmeng Rao, Google DeepMind; Budhendra Bhaduri,  Oak Ridge National Laboratory; Samantha T. Arundele, USGS)
• GeoAI and Deep Learning Symposium: The Convergence of Generative AI and GIScience: Framework, Foundation Models, Standards, and Infrastructure (Paper session; Contact: Zhenlong Li,  Penn State University (zhenlong@psu.edu);  Co-organizers: Huan Ning,  Emory University;   Song Gao,  University of Wisconsin-Madison;  Arif Masrur,  ESRI;  Temitope Akinboyewa,  Penn State University; Ruixiang Liu,  Penn State University; Ali Khosravi Kazazi,  Penn State University; Wenwen Li,  Arizona State University; Jinmeng Rao, Google DeepMind; Budhendra Bhaduri,  Oak Ridge National Laboratory; Samantha T. Arundele, USGS)
• GeoAI and Deep Learning Symposium: The Convergence of Generative AI and GIScience: Society Impacts, Education and Ethical Considerations (Paper session; Contact: Zhenlong Li,  Penn State University (zhenlong@psu.edu); Co-organizers: Huan Ning,  Emory University;   Song Gao,  University of Wisconsin-Madison;  Arif Masrur,  ESRI;  Temitope Akinboyewa,  Penn State University; Ruixiang Liu,  Penn State University; Ali Khosravi Kazazi,  Penn State University; Wenwen Li,  Arizona State University; Jinmeng Rao, Google DeepMind; Budhendra Bhaduri,  Oak Ridge National Laboratory; Samantha T. Arundele, USGS)
• GeoAI and Deep Learning Symposium: The Convergence of Generative AI and GIScience: Domain Applications and Use Cases (Paper session; Contact: Zhenlong Li,  Penn State University (zhenlong@psu.edu);  Co-organizers: Huan Ning,  Emory University;   Song Gao,  University of Wisconsin-Madison;  Arif Masrur,  ESRI;  Temitope Akinboyewa,  Penn State University; Ruixiang Liu,  Penn State University; Ali Khosravi Kazazi,  Penn State University; Wenwen Li,  Arizona State University; Jinmeng Rao, Google DeepMind; Budhendra Bhaduri,  Oak Ridge National Laboratory; Samantha T. Arundele, USGS)
• GeoAI and Deep Learning Symposium: The Convergence of Generative AI and GIScience: Benchmarking, Fine Tuning, and Evaluation (Paper session; Contact: Zhenlong Li,  Penn State University (zhenlong@psu.edu);  Co-organizers: Huan Ning,  Emory University;   Song Gao,  University of Wisconsin-Madison;  Arif Masrur,  ESRI;  Temitope Akinboyewa,  Penn State University; Ruixiang Liu,  Penn State University; Ali Khosravi Kazazi,  Penn State University; Wenwen Li,  Arizona State University; Jinmeng Rao, Google DeepMind; Budhendra Bhaduri,  Oak Ridge National Laboratory; Samantha T. Arundele, USGS)
• GeoAI and Deep Learning Symposium: GeoAI and the Future of African Urbanism: (Paper session; Contact: Isaac Quaye, Temple University (isaac.quaye@temple.edu), Co-organizers: Oforiwaa Pee Agyei-Boakye, University of Minnesota Twin Cities)
• GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence: (Paper session; Contact: Gengchen Mai, University of Texas at Austin (gengchen.mai@austin.utexas.edu) ; Co-organizers: Angela Yao, University of Georgia; Zhangyu Wang, University of Maine)
• GeoAI and Deep Learning Symposium: Geographic Biases and Transferability in GeoAI (Paper session; Contact: Zhiyong Zhou, University of Wisconsin–Madison, zhiyong.zhou@wisc.edu; Co-organizers: Song Gao, University of Wisconsin–Madison)
• GeoAI and Deep Learning Symposium: GeoAI for Spatial Analytics and Modeling (Paper session; Contact: Di Zhu, University of Minnesota (dizhu@umn.edu) ; 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: GeoAI for Resilient Urban Design for Natural Hazards and Human-Made Disasters (Paper session; Contact: Orhun Aydin, Saint Louis University (orhun.aydin@slu.edu) ; Co-organizer: Yingjie Hu, University at Buffalo)

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

Title: Spatial Networks and AI for Social Sensing

Abstract: Cities pulse with life. People, information, and resources move through streets and neighborhoods, weaving the spatial structures of human-environment interactions in cities. While the proliferation of spatiotemporal data and advances in computational methods have significantly enhanced our ability to sense human activities and characterize geographic places, the power of spatial networks that organize our urban systems is still underutilized. Focusing on three missing pieces in contemporary urban social sensing: connected places, invisible flows, and unknown mechanisms, Di’s talk will discuss how GeoAI and network-informed spatial analytics and modeling can reveal the structural relationships among people, places, and environments that give rise to complex spatiotemporal phenomena in cities.

Bio: Dr. Di Zhu is an Assistant Professor of GIScience in the Department of Geography, Environment, and Society at the University of Minnesota, Twin Cities (UMN) and the director of Geospatial Data Intelligence (GeoDI) Lab. He holds a Ph.D. in Cartology and GIScience, a B.S. in GIS, and a dual B.Ec. in Economics all from Peking University. Di’s research bridges Spatial Statistics, Geospatial Artificial Intelligence (GeoAI), and Spatiotemporal Social Sensing, focusing on the human-environment systems within urban socioeconomics, human mobility, transportation, population, etc. Beyond the main appointment at UMN, Dr. Zhu is a faculty member of the Minnesota Population Center, a CTS scholar at the Center for Transportation Studies, an executive committee member of the MGIS program, and an affiliated faculty at the Data Science Initiatives of UMN. Di was a visiting lecturer at SpaceTimeLab, University College London before joining UMN. He has published in top venues such as IJGIS, AAAG, TGIS, GeoInformatica, EPB, Scientific Data, ISPRS JPRS, TPAMI, ACM SIGKDD. He served as a panelist or reviewer for NSF CISE, HEGS, MMS and ES programs. He was the BOD member of CPGIS during 2023-2025. He served as a chair, local chair, or PC member for conferences, workshops, and sessions such as ACM SIGSPATIAL, AAG, GISRUK, GeoInformatics. He has won academic accolades such as the Rising Star Award of College GIS Forum in China, Top 20 WGDC 2022 Global Young Scientist Award, Early Career Award of GIS Research of the United Kingdom, Distinction of Doctoral Thesis of Peking University, etc. Di teaches in GIScience, spatial analysis, GeoAI, and spatial networks.

Calendar: https://today.wisc.edu/events/view/220038

Title: A New Era of Spatial Intelligence with GeoAI

Abstract: Drawing on ongoing GeoAI initiatives spanning public health, education, and urban analytics, this talk advances a vision of GeoAI as a next-generation paradigm for intelligent spatial decision-making. Through real-world case studies, including advanced computer vision integrated with urban visual analytics, GeoAI-enabled malaria intervention in East Africa, and Generative AI–supported geography education, the presentation illustrates how high-resolution satellite imagery, street-view data, foundation models, and community-engaged frameworks can transform geospatial information into actionable intelligence. It also critically examines emerging challenges, including data bias and quality, digital divides, geo-hallucinations, and AI–human perceptual mismatches. By coupling theory-aware modeling with responsible cyberinfrastructure and human-in-the-loop design, GeoAI can move beyond automation toward equitable, explainable, and socially grounded spatial decision intelligence in a rapidly evolving world.

Bio: Dr. Xiao Huang is an Assistant Professor in the Department of Environmental Sciences at Emory University. His research spans human–environment interactions, computational social science, urban informatics, GeoAI, and disaster remote sensing, with a strong focus on integrating artificial intelligence and geospatial technologies for societal impact. He has authored more than 240 peer-reviewed journal articles and over 20 book chapters, edited five books, and received more than 7,000 citations on Google Scholar. He is recognized among the World’s Top 2% Scientists by Stanford/Elsevier. Dr. Huang serves as Associate Editor for Computational Urban Science and the Journal of Remote Sensing, and sits on the editorial boards of several leading journals. His work has been featured by Nature News, NASA, NBC, and Fox. He has secured competitive funding from NSF, NASA, the Bill & Melinda Gates Foundation, and the National Academies, and is the recipient of the 2026 AAG Glenda Laws Award.

Calendar link: https://today.wisc.edu/events/view/219143

Personal website: https://envs.emory.edu/people/bios/huang-xiao.html

Title: Learning Earth’s Water Extremes: Geo-Foundation Models for Flood and Cryosphere Monitoring

Abstract: Monitoring floods, glaciers, and glacial lakes is of broad interest to the scientific community due to their direct impact on human lives, infrastructure, and freshwater availability. Recent advances in remote sensing and deep learning have enabled fast, automated, and reliable mapping of these Earth surface processes. However, the widespread application of deep learning remains limited by the scarcity of labeled data across diverse geographic regions. In this context, recent developments in geo-foundation models offer significant potential to generate robust and transferable maps in data-scarce settings by leveraging large-scale pretraining of model encoders. In this talk, I will present case studies on floods, glaciers, and glacial lakes using multi-source remote sensing data and geo-foundation models. Our experiments aim to inform end users about optimal model selection strategies under varying data availability scenarios. Additionally, I will explore the emerging potential of integrating vision models with large language models to further advance Earth observation capabilities. Overall, these examples demonstrate improved monitoring of two critical Earth system components—the cryosphere and the hydrosphere—and contribute to a better understanding of associated hazards and freshwater resources.

Bio: Dr. Saurabh Kaushik received the Ph.D. degree through a bi-national program between the Academy of Scientific and Innovative Research (AcSIR), India, and the German Aerospace Center (DLR), Germany. He is currently a Postdoctoral Research Associate at the University of Wisconsin-Madison, working on NSF and NASA funded flood mapping project. His research lies at the intersection of computer vision, remote sensing, and Earth observation, with a focus on glacial lakes, floods, glaciers, and water-resource risk assessment. His work aims to develop scalable, reliable Earth observation solutions for long-term environmental monitoring. To learn more about his publications and projects see: https://sk-2103.github.io/  

Title: GeoAI-enabled multi-scale cartography: progress and a research agenda

Abstract:

Multi-scale spatial representation is a crucial mechanism for describing facts about the real world and for managing and communicating spatial data efficiently and effectively. To enable such multi-scale spatial representation, map generalization has thus been developed. The advances of GeoAI, which focus on spatially oriented deep learning and process understanding with AI techniques, have brought about a new paradigm for map generalization. In this talk, I will present the latest research progress on GeoAI-enabled map generalization supported by the Swiss National Science Foundation (SNSF). This work spans the full GeoAI workflow: from geospatial problem formulation and spatial data modeling to spatially aware deep learning architectures and explainable AI techniques. Building on these developments, I will also discuss key technical challenges and research opportunities for applying GeoAI to multi-scale cartography, with the aim of informing and inspiring future innovation in the field.

Short bio:

Dr. Zhiyong Zhou is a Swiss postdoctoral research fellow and a visiting scholar at the GeoDS Lab, University of Wisconsin-Madison, which is supported by the SNSF Postdoc.Mobility Fellowship. Prior that, he was a postdoc at the Department of Geography, University of Zurich, Switzerland. He holds a Ph.D. in Geography/Earth System Science from the University of Zurich, as well as an M.E. degree and a B.E. degree with Honors from China University of Geosciences (Wuhan). His research focuses on human-centered geospatial AI. He primarily investigates human–space interactions and develop human-adaptive, spatially explicit techniques for spatial data generalization, smart mobility, and sustainable built environments. Additionally, he serves as vice-chair of the ICA Commission on Location-Based Services.

Personal website: https://www.zhouzhiyong.com/

Title

Enhancing AI’s Geospatial Intelligence: Multimodality, Spatial-Explicitness, and Explainability

Abstract

This talk focuses on three perspectives (i.e., data, methodology, and explainability) to discuss how we can enhance geospatial intelligence of AI models, a core question in the development of GeoAI. First, I will introduce a multimodal learning framework that fuses remote sensing imagery, LiDAR, and text/POIs. The framework consists of CityVerse, a curated geospatial multimodal benchmark, and CLIP4Geo, a geospatial vision-language model that aligns geographic coordinates, textual descriptions, and visual features to support various urban analytical tasks, highlighting the value of dataset quality over sheer scale. Next, I will present the design of spatially explicit graph neural networks that integrate mobility (OD flows) with geographic contiguity, and showcase their performance in epidemic forecasting. Results reveal city-specific regimes where hybrid mobility-contiguity graphs can outperform either alone. Finally, I will present Semantic4Safety, an explainable GeoAI pipeline for urban road safety: zero-shot semantic segmentation constructs interpretable streetscape indicators from street view images, coupled with XGBoost, SHAP, and causal effect estimation on geo-located incidents to yield actionable, type-specific risk factors. By demonstrating these perspectives and case studies, this talk aims to facilitate the roadmap for developing Geo-Foundation Models in the future. 

Short Bio

Dr. Meiliu Wu is a Lecturer (Assistant Professor) in Geospatial Data Science at the University of Glasgow (UoG), where she directs the new MSc program in Geospatial Data Science and AI and leads the GIFTS Lab (Geospatial Intelligence for Future Technology and Sustainability Lab). She earned her PhD in Geography from the University of Wisconsin-Madison in May 2024 and joined UoG in August 2024. Dr. Wu’s research interests include GIScience, geospatial AI (GeoAI), debiasing, urban analytics, and environmental sciences, with her work featured in leading journals/conferences and funded by UKRI and the Alan Turing Institution. She is a Fellow of Royal Geographical Society with IBG, serving as a Committee Executive Member of GIScience Research Group.

Profile webpage: https://www.gla.ac.uk/schools/ges/staff/meiliuwu/

Personal webpage: https://meiliuwu.github.io/

Congratulations to our lab member Qianheng ZHANG (together with Geography PhD student Yanbing Chen) jointly won the 27th Annual Student Dynamic Map Competition at the North American Cartographic Information Society (NACIS) 2025 annual conference.

NACIS recognizes the importance of digital and dynamic interactive mapping in Cartography by hosting this competition.The winning team project is the Lyriscape of Cantopop (Hong Kong Music Atlas):

https://qianhengzhang.github.io/HongKongMusicAtlas/#/

GeoDS Lab is excited to welcome two outstanding postdoc research fellows Dr. Ardiantiono and Dr. Zhiyong Zhou joining our group this Fall!

Ardiantiono, University Distinguished Research Fellow, RISE Initiative

I am a conservation scientist focused on advancing evidence-based conservation in tropical ecosystems. I’ve been fortunate to work with incredible species—from tigers and elephants to Komodo dragons and hornbills. My work centers on optimizing biodiversity monitoring in human-dominated landscapes, fostering human–wildlife coexistence, and strengthening community-based conservation. Working with Prof. Zuzana Burivalova from Sound Forest Lab and Prof. Song Gao from Geospatial Data Science Lab, I’m developing an AI-based framework to integrate camera trap, acoustic, and eDNA data for monitoring the biodiversity benefits of Natural Climate Solutions. I currently serve as President of the Society for Conservation Biology Indonesia (2023-2025) and as an Associate Editor for the Journal of Applied Ecology.

Zhiyong Zhou, Swiss NSF Postdoc Research Fellow

I am a postdoctoral research fellow supported by the Swiss National Science Foundation (SNSF) through the Postdoc.Mobility Fellowship. Prior to joining the GeoDS Lab, I was a postdoc at the Department of Geography, University of Zurich, Switzerland. I hold a Ph.D. in Geography/Earth System Science from the University of Zurich, as well as an M.E. degree and a B.E. degree with Honors from China University of Geosciences (Wuhan). My research focuses on human-centered geospatial AI. I primarily investigate human–space interactions and develop human-adaptive, spatially explicit techniques for spatial data generalization, smart mobility, and sustainable built environments. Additionally, I serve as vice-chair of the ICA Commission on Location-Based Services.

Title: Trajectory Privacy Protection with Geospatial AI

Abstract: The prevalence of Location-Based Services (LBS) has led to the generation of large amounts of individual-level trajectory data, which offers opportunities to study human mobility patterns, human-environment interactions, disaster responses, and public health issues. However, trajectory big data also pose significant challenges related to geoprivacy protection and broader social and ethical implications. In this talk, we will discuss three main challenges in trajectory privacy protection, namely the trade-off between privacy and utility, data sparsity and imbalance issues, and endogenous privacy risks in centralized structures. We will also introduce some of our recent work to demonstrate the potential of Geospatial AI in addressing these challenges in trajectory privacy protection.

Bio: Dr. Jinmeng Rao is an AI researcher at Google DeepMind. He was an AI researcher at Google[X]. He got his MS in Computer Sciences and PhD in Geography at UW-Madison supervised by Prof. Song Gao. He holds a M.S. and B.S. in Cartography and GIS from Wuhan University. His main research interest is Geospatial AI and geoprivacy. His articles have been published in top journals and conferences, including IJGIS, TGIS, GIScience, ACM SIGSPATIAL, ACM SIGIR, AAAI, etc. He serves as a reviewer or for the program committee for various journals and conferences, including IJGIS, Annals of GIS, TGIS, JAG, Computers & Geosciences, NeurIPS, ACL, etc. He received the 2024 AAG William L. Garrison Award for Best Dissertation in Computational Geography.

Title: Human-centered GeoAI in the era of Generative AI: Perceptions and Creativity

Abstract: The emergence of Generative AI offers numerous opportunities to benefit geospatial intelligence, enabling novel ways to advance our knowledge of human perceptions and creativity. In Dr. Kang’s talk, he will explore the impact of Generative AI on geospatial analytics through two key perspectives. First, he will discuss how a Soundscape-to-Image model could translate and visualize human perceptions of visual and acoustic environments. Second, he will illustrate how generative AI, through the process of data-style separation, can produce not only accurate but also visually appealing maps that adhere to ethical standards in cartography. His talk will delve into the transformative potential of Generative AI in the development of Human-centered GeoAI. 

Bio: Dr. Yuhao Kang is a tenure-track Assistant Professor, directing the GISense Lab at the Department of Geography and the Environment, The University of Texas at Austin. He was a postdoctoral researcher at the MIT SENSEable City Lab, received his Ph.D. from the GeoDS Lab, University of Wisconsin-Madison, and obtained his bachelor’s degree from Wuhan University. Before joining UT-Austin, he had working experience at the University of South Carolina, Google X, and MoBike. He was the founder of the non-profit educational organization GISphere that promotes global GIS education. Dr. Kang’s research mainly focuses on Human-centered Geospatial Data Science to understand human experience at place and develop ethical and responsible geospatial artificial intelligence (GeoAI) approaches. He was the recipient of the Waldo-Tobler Young Researcher Award by the Austrian Academy of Sciences, CaGIS Rising Award, CPGIS Education Excellence Award, etc.

Title: Spatial Representation Learning: What, How, and Why

Abstract: Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, networks, images, etc.) in their native formats. Learning good spatial representations is a fundamental problem for various downstream applications such as species distribution modeling, weather forecasting, trajectory generation, geographic question answering, etc. In this presentation, we will discuss several recent works from UT SEAI Lab about spatial representation, including various location encoding models (Space2Vec and Sphere2Vec), an SRL deep learning framework (TorchSpatial), and a SRL-powered geo-foundation model (GAIR). We will discuss 1) WHAT is location representation learning? 2) HOW to develop location representation learning models? and 3) WHY do we need them?

Bio: Dr. Gengchen Mai is currently a Tenure-Track Assistant Professor at the Department of Geography and the Environment, University of Texas at Austin. He got his Ph.D. in GIScience from UCSB Geography. Before becoming a faculty, he was a Postdoc at Stanford Computer Science. Before joining UT, he was an Assistant Professor at the University of Georgia. Dr. Mai’s research is Spatially Explicit Artificial Intelligence, Geo-Foundation Models, Geographic Knowledge Graphs, etc. Dr. Mai’s work has been published not only in many top geography/GIScience/Remote Sensing journals but also in many ML/AI conferences such as NeurIPS, ICML, ICLR, ACM SIGIR, ACM SIGSPATIAL, etc. He is the recipient of many prestigious awards including AAG 2021 Dissertation Research Grants, AAG 2022 William L. Garrison Award for Best Dissertation in Computational Geography, AAG 2023 J. Warren Nystrom Dissertation Award, Top 10 WGDC 2022 Global Young Scientist Award, the Jack and Laura Dangermond Graduate Fellowship, UT MGCE Fellowship, 2025 Geospatial Rising Star Award, etc. He is currently the registration chair of ACM SIGSPATIAL 2025, vice chair of AAG GISS Specialty Group, and PC member for NeurIPS, ICML, ICLR, WWW, AISTATS, ACM SIGIR, ACM SIGSPATIAL, GIScience, etc.

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.

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

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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/

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

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.

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. 

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.

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.

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