Geospatial Data Science Lab
Congratulations to our GeoDS lab PhD student Qianheng Zhang, who just won the 1st place in the “Best Student Honors Paper Competition” of the Geographic Information Science and Systems Specialty Group (GISS-SG) at AAG 2026!
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)
This symposium is sponsored by: AAG GISS, SAM, and CISG specialty groups.
Understanding the interaction between complex urban environments and human mobility flow patterns underpins adaptive transport systems, resilient communities, and sustainable urban developments, yet inter-regional origin-destination mobility flow information from traditional surveys are costly to update. The satellite imagery offers up-to-date information on urban sensing and opens avenues to examine urban morphology-mobility dynamics. This study develops a deep learning model, Imagery2Flow for predicting fine-grained human mobility flows in urban areas using 10 to 30-meter medium resolution satellite imagery in a timely and low-cost manner. Extensive experiments demonstrate good performance and flexible spatial-temporal generalizability on the top-10 largest metropolitan statistical areas of the United States. Through exploring the spatial heterogeneous effects, we investigate the urban factors (centrality and compactness) influencing human movement flow distributions, enhancing our comprehension of their interactions. The spatial transferability of Imagery2Flow helps reduce regional inequality by informing decisions in data-poor regions, learning from data-rich ones. Interestingly, the typologies of urban sprawl can help explain the cross-city model generalization capability. The temporal transferability proves that human dynamics of cities and the process of urbanization can be well captured from the observed built environment by remote sensing.
Xu, Y., Gao, S.*, Huang, Q., Göçmen, A., Zhu, Q., & Zhang, F*. (2025). Predicting human mobility flows in cities using deep learning on satellite imagery. Nature Communications, 16(1), 10372. https://doi.org/10.1038/s41467-025-65373-z
The code of Imagery2Flow is publicly available at GitHub: https://github.com/GeoDS/Imagery2Flow
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/
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!
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.
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.
The Thirteenth International Conference on Geographic Information Science (GIScience 2025) will be held in Christchurch, New Zealand, on 26-29 August 2025, hosted by the University of Canterbury in collaboration with the GIScience academic research community across New Zealand. GIScience 2025 is the flagship conference in geographic information science and continues the highly successful conference series which started in 2000.
The conference regularly attracts over 250 international participants from academia, industry, and government to discuss and advance the state-of-the-art in geographic information science. August 26 is dedicated to Workshops and Tutorials. The main conference runs from August 27 to 29 and includes a single refereed paper track and an abstract track for posters and demo submissions. GIScience research spans the gamut of interrelated discovery activities related to geographic information from the invention of new computational instruments, the gathering of data via observation or experimentation, and discovery of descriptive generalizations patterns in data through to the creation of explanatory theories and the testing of theories. The GIScience conference series seeks submissions that make fundamental advances to the field through this multifaceted process.
GIScience 2025 welcomes papers, posters and demos covering emerging topics and fundamental research findings across all sectors of geographic information science, including (but not limited to) the role of geographic information in geography, computer science, engineering, information science, linguistics, mathematics, cognitive science, philosophy, psychology, social science, and geostatistics. We welcome participation from community members sharing work at various stages of development, including position pieces, works in progress, as well as full papers for inclusion in the conference proceedings.
Proceedings papers Deadline: January 31, 2025
Abstracts and Demos Deadline: April 4, 2025
Conference Website: https://giscience2025.org
CFP: 2025 AAG Symposium on GeoAI and Deep Learning for Geospatial Research
2025 AAG Annual Meeting, Detroit, MI, March 24-28, 2025
Lead Organizers:
Song Gao, University of Wisconsin, Madison
Wenwen Li, Arizona State University
Yingjie Hu, University at Buffalo
Budhu Bhaduri, Oak Ridge National Laboratory
Orhun Aydin, Saint Louis University
Shawn Newsam, University of California, Merced
Samantha T. Arundel, United States Geological Survey
Gengchen Mai, University of Texas Austin
Krzysztof Janowicz, University of Vienna
The field of GeoAI is advancing at an astonishing speed. We are excited to witness the significant growth of GeoAI in terms of its methods, its diverse geospatial applications, and its increasing societal impacts. For example, GeoAI has been applied to advance our understanding of environmental and climate change on the earth, improve individual and population health, enhance community resilience in natural disasters, strengthen smart and connected communities, more accurately predict spatiotemporal traffic flows, support humanitarian mapping and policymaking, and more. From the perspective of methodological development, we have observed a paradigm shift from using task-specific models with supervised learning to leveraging the power of visual foundation models, large language models (LLMs), and multimodal foundation models to achieve zero-shot to few-shot geospatial learning. We have also seen an increasing body of pioneering research integrating spatial theories and principles into general AI model design to develop “spatialized” AI that best tackles spatial and spatiotemporal problems.
Building on the success of previous AAG GeoAI symposiums and continuing to push the cutting edge of GeoAI research and its societal impact, the 2025 symposium aims to bring together geographers, GI scientists, remote sensing scientists, computer scientists, health researchers, urban planners, transportation professionals, disaster response experts, ecologists, earth system scientists, stakeholders, and many others to share recent research outcomes and discuss challenges for GeoAI research in the following years.
Sessions (all sessions will be accessed at: https://bit.ly/aag2025geoai):
To present your research in one of these sessions, please register and submit your abstract at https://aag.secure-platform.com/aag2025/. When you receive confirmation of your submission, please forward your confirmation email to the session organizers by November 14, 2024.
We are very glad to invite you to mark your calendar for joining the forthcoming Geospatial Data Science Speaker Series 2024-2025 events, which are hosted by the GeoDS lab in Geography and co-sponsored by the Data Science Institute @UW-Madison.
The first event of this semester will be jointly with the Geography Yi-Fu Lectures. We will first have Dr. Kevin Mwenda, an Associate Professor of Population Studies (Research) at the Population Studies and Training Center (PSTC) and the Director of the Spatial Structures in the Social Sciences (S4), visiting UW-Madison and will present “Beyond Maps: Integrating Place and Space for Community Resilience” at 3:30 p.m.-4:30p.m., on October 18, 2024 (Friday), Science Hall 180.
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:
Type: Panel Date: 4/16/2024
Type: Panel Date: 4/17/2024
Type: Panel Date: 4/17/2024
Type: Panel Date: 4/18/2024
Type: Paper Date: 4/16/2024
Presenter: Yuhan Ji
Type: Paper Date: 4/18/2024
Type: Paper Date: 4/18/2024
Presenter: Qianheng Zhang
Type: Paper Date: 4/18/2024
Presenter: Jake Krue
Type: Paper Date: 4/18/2024
Primary Organizer: Jinmeng Rao, Google DeepMind
Type: Paper Date: 4/19/2024
Primary Organizer: Yuhao Kang, University of South Carolina
Type: Paper Date: 4/19/2024
Presenter: Yichen Xu
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.
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:
By Song Gao, Yingjie Hu, Wenwen Li
By Helen Couclelis
By Krzysztof Janowicz
By Song Gao, Jinmeng Rao, Yunlei Liang, Yuhao Kang, Jiawei Zhu, Rui Zhu
By Samantha T. Arundel, Kevin G. McKeehan, Wenwen Li, Zhining Gu
By Gengchen Mai, Ziyuan Li, Ni Lao
By Di Zhu, Guofeng Cao
By Yiqun Xie, Xiaowei Jia, Weiye Chen, Erhu He
By Ximeng Cheng, Marc Vischer, Zachary Schellin, Leila Arras, Monique M. Kuglitsch, Wojciech Samek, Jackie Ma
By Kai Sun, Yingjie Hu, Gaurish Lakhanpal, Ryan Zhenqi Zhou
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
By Tao Cheng, James Haworth, Mustafa Can Ozkan
By Philipe A. Dias, Thomaz Kobayashi-Carvalhaes, Sarah Walters, Tyler Frazier, Carson Woody, Sreelekha Guggilam, Daniel Adams, Abhishek Potnis, Dalton Lunga
By Lei Zou, Ali Mostafavi, Bing Zhou, Binbin Lin, Debayan Mandal, Mingzheng Yang, Joynal Abedin, Heng Cai
By Andreas Züfle, Taylor Anderson, Hamdi Kavak, Dieter Pfoser, Joon-Seok Kim, Amira Roess
By Chishan Zhang, Chunyuan Diao, Tianci Guo
By Filip Biljecki
By Peter Kedron, Tyler D. Hoffman, Sarah Bardin
By Grant McKenzie, Hongyu Zhang, Sébastien Gambs
By Bo Zhao, Jiaxin Feng
By Krzysztof Janowicz, Kitty Currier, Cogan Shimizu, Rui Zhu, Meilin Shi, Colby K. Fisher, Dean Rehberger, Pascal Hitzler, Zilong Liu, Shirly Stephen
By Shawn Newsam
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):
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.