GeoDS Seminar

Title: From Space to Health: Satellites and AI for Environmental Exposure Assessment.

Abstract: Environmental exposures play an important role in human health, but traditional ground monitoring networks have limited coverage and often miss underserved areas. Satellite-based remote sensing techniques, combined with AI algorithms, now make it possible to estimate exposures more consistently across space and time. This talk will introduce a spatial data science approach that integrates satellite data with classical machine learning models to assess environmental exposures from local community to global scales.

Bio: Dr. Hang is a tenure-track Assistant Professor of Environmental and Occupational Health with a joint appointment in Biostatistics and Data Science. She earned her Ph.D. in Environment and Resources at the University of Wisconsin–Madison, complemented by an M.S. in Atmospheric and Oceanic Sciences and a Graduate Certificate in Energy Analysis and Policy. She completed her postdoctoral fellowship in Environmental Health at Emory University’s Rollins School of Public Health. At UTHealth, she directs the Health-Atmosphere Nexus Group (utsph-hang.org), combining satellite remote sensing, artificial intelligence, and community insights to examine how air pollution, water quality, extreme weather events, and related environmental stressors impact human health from local neighborhoods to global populations. Her work has been supported by NASA, NIH, NSF, CDC, NASEM, and CPRIT. Dr. Hang teaches Environmental Epidemiology, an interdisciplinary course engaging students from public health, medicine, and nursing. Her trainees have received prestigious national honors, including the NSF Graduate Research Fellowship and NASA’s Future Investigators Award. She also serves as a Council Member of the American Geophysical Union, advocating for early-career researchers and facilitating international scientific collaborations.

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: Spatio-Temporal Data Mining: From Deep Learning to Foundation Models

Abstract: Spatio-temporal (ST) data, which integrates spatial (location-based) and temporal (time-based) information, is the key to improving many real-world applications with high social impacts, such as transportation, logistics, climate, and energy. This talk presents the evolution of spatio-temporal AI, transiting from task-specific deep learning models to the emerging paradigm of foundation models. I will highlight my research progress in modeling human mobility behavior and logistics, specifically focusing on methodologies such as DeepRoute , Graph2Route , and DRL4Route for route prediction and DiffSTG for probabilistic graph forecasting. Furthermore, I will introduce LaDe, the first comprehensive last-mile delivery dataset from industry. Of particular focus in this talk is the recent progress in Spatio-Temporal Foundation Models (STFMs), including the Taxonomy of current STFM and future research opportunities.

Bio: Haomin Wen is postdoctoral researcher  at CMU Data Analytics Techniques Algorithms (DATA) Lab. He was a joint PhD at National University of Singapore, received his Ph.D. from INSIS Lab,  Beijing Jiaotong university.  Dr. Wen’s research mainly focuses on spatial-temporal data mining, human mobility learning to understand the patterns of human’s movements especially in the logistics system. His articles have been published in top journals and conferences, including TKDE, TITS, KDD, ACM SIGSPATIAL, AAAI, etc. He is the founder of Overleaf Copilot.  He serves as a reviewer or for the program committee for various journals and conferences, including KDD, IJCAI, ICML, etc.

Calendar link: https://today.wisc.edu/events/view/217566

Personal website: https://wenhaomin.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/

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. 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.

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 and students, 

Greetings!  I am very glad to invite you to mark your calendar for joining the forthcoming Geospatial Data Science Speaker Series Spring 2023 events, which are hosted by the GeoDS lab in Geography and co-sponsored by the Data Science Institute, UniverCity Alliance, and GISPP @UW-Madison.  We will have Dr. Filip Biljecki, the Director of Urban Analytics Lab from the National University of Singapore visit UW-Madison 11:45 a.m.-1 p.m., on March 28, 2023 (Tue), Science Hall 110 and Dr. Fabio Duarte from the MIT Senseable City Lab on April 13 (Thur), Science Hall 140. Pizza lunch and coffee will be provided in the events. 

Dr. Clio Andris (Assistant Professor of City & Regional Planning and Interactive Computing) from Georgia Tech was invited to the renowned Yi-Fu Lecture at UW-Madison Geography. Our GeoDS Lab was honored to host Dr. Andris’ visit and had a great conversation on collaborative projects and joint research.