Investigators: Qunying Huang (Principal Investigator), Song Gao (Co-PI), Robert B Pierce (Co-PI)
Abstract: Wildfires are increasing in frequency, intensity, and societal impact as warming temperatures, increasing aridity, and more extreme weather alter fire-prone environments across the United States and globally. These changes threaten communities, ecosystems, air quality, and critical infrastructure. Despite advances in wildfire observation and modeling, major scientific questions remain regarding how weather, vegetation, topography, and human activities interact to influence wildfire ignition, spread, and suppression. This project will develop an integrated geospatial artificial intelligence framework to improve wildfire monitoring, forecasting, and suppression planning while advancing understanding of wildfire–environment–human interactions. The project will produce open datasets, models, and computational tools to support future wildfire research, contribute to training students in geospatial data science and Earth system science, and accelerate translation of research advances into operational wildfire management.
This project will develop an integrated geospatial artificial intelligence framework for wildfire detection, spread prediction, and cooperative wildfire control through three research objectives. First, it will fuse high-temporal-resolution Geostationary Operational Environmental Satellite (GOES) observations with high-spatial-resolution Visible Infrared Imaging Radiometer Suite (VIIRS) imagery using a diffusion-based super-resolution model to generate enhanced active fire observations and enable rapid fire detection from time-series imagery. Second, it will develop a physics-guided spatial–temporal graph convolutional network that combines physical constraints with data-driven learning to improve wildfire spread prediction. Third, it will develop knowledge-guided deep reinforcement learning models that integrate fire management knowledge with weather, fuels, terrain, transportation networks, and population distributions to support adaptive suppression strategies and resource allocation. Using observations and case studies from more than 100 large wildfire events across the United States, the project will generate new insights into wildfire dynamics and improve understanding of the climatic, environmental, geographic, and human factors shaping wildfire behavior.
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.
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.
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.
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):
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.
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.
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
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!
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.
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
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.
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.
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.
Organizers: Rafael Pires de Lima, rlima@colorado.edu, University of Colorado Boulder; Co-organizers: Morteza Karimzadeh, University of Colorado Boulder, Guofeng Cao, University of Colorado Boulder, Andong Ma, University of Colorado Boulder
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)
Organizers: Xiao Li, xiao.li@ouce.ox.ac.uk, University of Oxford; Co-organizers: Xiao Huang, University of Arkansas, Haowen Xu, Oak Ridge National Laboratory, Yuhao Kang, University of Wisconsin, Madison
Organizers: Gengchen Mai, gengchen.mai@gmail.com, University of Georgia; Co-organizers:Angela Yao, University of Georgia; Yao-Yi Chiang, University of Minnesota-Twin Cities; Zhangyu Wang, University of California Santa Barbara
Organizers: Gengchen Mai, gengchen.mai@gmail.com, University of Georgia; Co-organizers:Angela Yao, University of Georgia; Yao-Yi Chiang, University of Minnesota-Twin Cities; Zhangyu Wang, University of California Santa Barbara
Organizers: Gengchen Mai, gengchen.mai@gmail.com, University of Georgia; Co-organizers:Angela Yao, University of Georgia; Yao-Yi Chiang, University of Minnesota-Twin Cities; Zhangyu Wang, University of California Santa Barbara
Organizers: Di Zhu, dizhu@umn.edu, University of Minnesota; Co-organizer: Guofeng Cao, University of Colorado, Boulder; Song Gao, University of Wisconsin, Madison
Organizers: Bing Zhou, spgbarrett@tamu.edu, Texas A&M University. Co-organizers: Lei Zou, Texas A&M University;Yingjie Hu, University at Buffalo; Marcela Suárez, Penn State University
Organizers: Yuhao Kang, yuhao.kang@wisc.edu, University of Wisconsin, Madison; Co-organizer: Fan Zhang, cefzhang@ust.hk, Hong Kong University of Science and Technology
Recently, Prof. Song Gao was invited to join the Associate Editors team ofInternational 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 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 thetop 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 oftop 2% highly cited scientists based on Stanford University’s analysis of Scopus data provided by Elsevier.
