My name is Yingjie (Jason) Hu. I am an Associate Professor in the Department of Geography at the University at Buffalo (UB), The State University of New York (SUNY). I am also
an Adjunct Professor in the Department of Computer Science and Engineering, and an Affiliate Faculty of the Center for Geological and Climate Hazards and the AI and Data Science Institute at UB.
My research expertise is in geospatial AI (GeoAI), natural hazards, geographic information science, and disaster management. I lead the GeoAI Lab@UB,
a research group focusing on integrating geospatial data, GIS, and AI methods to understand human-environment interactions under disaster contexts and to address disaster-related challenges.
The disasters we have studied include wildfires, winter storms, and hurricanes. Because disasters are highly complex, our research is interdisciplinary by nature and we frequently interact and collaborate with researchers from Civil and Environmental Engineering, Earth System Science, Environmental Science, and Computer Science and Engineering.
The research of my group contributes to better understandings of disaster-related human-environment interactions and helps build a more resilient and sustainable society.
Prospective Students: If you are interested in working with me, please check this Opportunities page to see whether there is an opening.
Research Expertise
Research Areas:
Natural Hazards
Human-Environment Interactions
Disaster Management
Community Resilience
Research Methods:
Geospatial AI (GeoAI)
GIS
Remote Sensing
Spatial Analysis
Honors and Awards
Community Champion, by University Consortium for Geographic Information Science (UCGIS) and NSF I-GUIDE Institute, 2025
World's Top 2% Scientists, by Stanford University and Elsevier, 2025
Reades, J., Hu, Y., Tranos, E., & Delmelle, E., (2025): The city as text. Nature Cities, 1-7.
Li, W., Arundel, S., Gao, S., Goodchild, M., Hu, Y., Wang, S., & Zipf, A. (2024): GeoAI for Science and the Science of GeoAI. Journal of Spatial Information Science, 29, 1-17.
Andris, C., Ayers, E., Grossner, K., Hu, Y., Hart, K., Thatcher, J., Tally Jr, R.T. & Giordano, A. (2020): Towards geospatial humanities: reflections from two panels. International Journal of Humanities and Arts Computing, 14(1-2), 6-26.
Hu, Y.* & Wu, J. (2010): Design of enterprise three-dimensional GIS based on skyline and ExtJS, Science of Surveying and Mapping, 35 (6), 247-249. (in Chinese)
Li, J., Wu, J., & Hu, Y. (2009): Development of three-dimensional urban scanning system using TerraExplorer Pro, Computer Technology and Development, 6, 66-68. (in Chinese)
Sun, K., Hu, Y.*, Lakhanpal, G., & Zhou, R.Z. (2023): Spatial cross-validation for GeoAI, In S. Gao, Y. Hu, and W. Li (Eds), Handbook of Geospatial Artificial Intelligence, Taylor & Francis Group.
Zhu, D. & Hu, Y. (2022): Artificial Intelligence, In L. Lees and D. Demeritt (Eds), Concise Encyclopedia of Human Geography, Edward Elgar Publishing.
Hu, Y.*, Li, W., Wright, D., Aydin, O., Wilson, D., Maher, O, & Raad, M. (2019): Artificial intelligence approaches. In J. P. Wilson (Eds), The Geographic Information Science & Technology Body of Knowledge, University Consortium for Geographic Information Science.
Hu, Y.* (2018): Geospatial semantics. In B. Huang, T. J. Cova, & M. Tsou et al. (Eds), Comprehensive Geographic Information Systems, also included in Elsevier’s Reference Module in Earth Systems and Environmental Sciences, 1, 80-94, Elsevier.
Janowicz, K., McKenzie, G., Hu, Y., Zhu, R., & Gao, S. (2018): Using semantic signatures for social sensing in urban environments, In C. Antoniou, L. Dimitriou, & F. Pereira (Eds), Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling, 31-54, Elsevier.
Hu, Y.* & Li, W. (2017): Spatial data infrastructures. In J. P. Wilson (Eds), The Geographic Information Science & Technology Body of Knowledge, University Consortium for Geographic Information Science.
Hu, Y.* & Janowicz, K. (2016): The semantic trajectory pattern, in P. Hitzler, A. Gangemi, K. Janowicz, A. Krisnadhi, & V. Presutti (Eds), Ontology Engineering with Ontology Design Patterns: Foundations and Applications, IOS Press 327-333.
Mai, G., Cundy, C., Choi, K., Hu, Y., Lao, N. and Ermon, S., (2022): Towards a foundation model for geospatial artificial intelligence. In: Proceedings of the 30th International Conference on Advances in Geographic Information Systems, (ACM SIGSPATIAL GIS), Article 106, Nov. 1-4, Seattle, USA.
Krisnadhi, A., Hu, Y., Janowicz, K., Hitzler, P., Arko, R., Carbotte, S., Chandler, C., Cheatham, M., Fils, D., Finin, T., Ji, P., Jones, M., Karima, N., Lehnert, K., Mickle, A., Narock, T., O'Brien, M., Raymond, L., Shepherd, A., Schildhauer, M., & Wiebe, P. (2015): The GeoLink modular oceanography ontology, In: Proceedings of the 14th International Semantic Web Conference, Oct. 11-15, 2015, Bethlehem, Pennsylvania, USA.
Krisnadhi, A., Arko, R., Carbotte, S., Chandler, C., Cheatham, M., Hitzler, P., Hu, Y., Janowicz, K., Ji, P., Karima, N., Shepherd, A., & Wiebe, P. (2015): R2R+BCO-DMO - linked oceanographic datasets, In: Proceedings of Diversity++ Workshop at the 14th International Semantic Web Conference, Oct. 11-15, 2015, Bethlehem, Pennsylvania, USA.
Hu, Y.*, Janowicz, K., Carral, D., Scheider, S., Kuhn, W., Berg-Cross, G., Hitzler, P., Dean, M., & Kolas, D. (2013): A geo-ontology design pattern for semantic trajectories, In Proceedings of the 2013 Conference On Spatial Information Theory, Sept. 2 - 6, 2013, Scarborough, North Yorkshire, UK.
Zhong, H., Wu, J., Li, L., Lv, Z., Hu, Y., & Yu, B. (2010): Mobile and wireless GIS based upon independent development, In Proceedings of 2010 International Conference on Electrical and Control Engineering, Jun. 25-27, 2010, Wuhan, Hubei, China.
Lv, Z.,Hu, Y., Zhong, H., Yu, B., Wu, J., Li, B., & Zhao, H (2010): Spatial indexing of global geographical data with HTM, In Proceedings of the 18th International Conference on Geoinformatics, Jun. 18-21, 2010, Beijing, China.
McKenzie, G. & Hu, Y. (2017): The “Nearby” exaggeration in real estate, in Proceedings of the Workshop on Cognitive Scales of Spatial Information, Sep. 4, 2017, L'Aquila, Italy.
Mai, G., Janowicz, K., Hu, Y., Gao, S. (2016): ADCN: an Anisotropic Density-based Clustering Algorithm, in Proceedings of the 24th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), Oct. 31-Nov 3, Burlingame, California, USA.
Hu, Y.*, Janowicz, K., Hitzler, P., & Sengupta, K. (2015): The Semantic Web Journal as Linked Data, In: Proceedings of 2015 International Semantic Web Conference (demos and posters track), Oct. 11-15, 2015, Bethlehem, Pennsylvania, USA.
Janowicz, K., Krisnadhi, A., Hu, Y., Suh, S., Weidema, B., Rivela, B., Tivander, J., Meyer, D., Berg-Cross, G., Hitzler, P., Ingwersen, W., Kuczenski, B., Vardeman, C., Ju, Y., & Cheatham, M. (2015): A minimal ontology pattern for life cycle assessment data, In: Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns at 2015 International Semantic Web Conference, Oct. 11-15, 2015, Bethlehem, Pennsylvania, USA.
Yan, B., Hu, Y., Kuczenski, B., Janowicz, K., Ballatore, A., Krisnadhi, A., Ju, Y., Hitzler, P., Suh, S., & Ingwersen, W. (2015): An ontology for specifying spatiotemporal scopes in life cycle assessment, In: Proceedings of the Diversity++ Workshop at 2015 International Semantic Web Conference, Oct. 11-15, 2015, Bethlehem, Pennsylvania, USA.
Abdalla, A., Hu, Y., Carral, D., Li, N., & Janowicz, K. (2014): An ontology design pattern for activity reasoning, In Proceedings of 5th Workshop on Ontology and Semantic Web Patterns, Oct. 18, 2014, Riva del Garda, Trentino, Italy.
(Total amount: ~ $1.5 million; my share: ~ $1.2 million)
PI, National Science Foundation (Award No.: 2416886), “Natural Disasters and Spatial Disparities in Community Resilience: Disrupted Human Mobility, Help Requests, and Voluntary Support.” , $400,000, 2024-2027.
PI, Cornell University (Subaward; Primary: Novartis Foundation; Award No.: 231850), “Data-Driven Approach to Identify Determinants of Outcomes and Risk Factors of Cardiovascular Diseases in New York City Area.” $27,815, 2024-2025.
Co-PI and UB PI, National Science Foundation (Award No.: 2401276), “ATD: Multiscale Anomaly Detection in Spatio-Temporal Multilayer Networks Encoding Human Mobility.” $250,000, 2023-2027.
PI, National Science Foundation (REG Supplement Award; Main Award No.: 2117771), “Understanding Community Resilience in the 2021 Texas Winter Storm through Human Mobility Data and its Association with Locations Reported on Social Media.” $6,000, 2022-2023.
PI, National Science Foundation (Award No.: 2117771), “Geospatial Artificial Intelligence Approaches for Understanding Location Descriptions in Natural Disasters and Their Spatial Biases.” $378,940, 2021-2026.
Co-I and GeoAI Lead, National Aeronautics and Space Administration (Award No.: 80NSSC21K1183), “Near Real-time Forecasting and Change Detection for a Fire Prone Shrubland Ecosystem.” $483,239, 2021-2026.
PI, Microsoft AI for Earth, “Near Real-time Forecasting and Change Detection for an Open Ecosystem by Integrating Artificial Intelligence and Ecological Modeling.” $15,000, 2020-2022.
PI, American Association of Geographers, “Workshop on Integrating Machine Learning into Geographic Research.” $1,600, 2020-2021.
PI, University of Tennessee, “How Can Local Housing Advertisements Help Disaster Response? A Geospatial Semantic Framework for Enriching Digital Gazetteers with Vernacular Place Names.” $3,334, 2017-2018.
PI, University of California, Santa Barbara, “Measuring the Value of Geographic Information in a Mobile Environment.” $3,038, 2015-2016.
Invited Talks
Invited Panelist, “Integrating Movement Data and AI Methods for Advancing Disaster Management”, Panel on Movement Analytics in the Era of Big Data, AI, and Open Science, March 18, 2026, San Francisco, California, USA.
Invited Panelist, “Thoughts on GeoAI and GIScience”, Panel on Past, Present, and Future of GIScience, March 17, 2026, San Francisco, California, USA.
Invited Talk, “Advancing Disaster Management by Integrating Geospatial Data and AI Methods”, Department of Civil, Structural, and Environmental Engineering, University at Buffalo, February 27th, 2026, Buffalo, NY, USA.
Invited Panelist, “Innovations in addressing compound risks”, Workshop on compounding, cascading, and critical risks to U.S. infrastructure and security, August 4-6, 2025, Portland, Oregon, USA.
Invited Panelist, “Understanding human-environment interactions for disaster management and sustainability solutions”, Spatial AI and Data Science Symposium at the AAG Annual Meeting, March 24, 2025, Detroit, Michigan, USA.
Invited Talk, “Extracting location descriptions from disaster-related messages using geo-knowledge-guided LLMs”, NSF I-GUIDE Community Champion Program Virtual Consulting Office Series, March 11, 2025, Online.
Invited Talk, “Generative AI, GIScience, and Disaster Management”, The 14th International Lectures of the International Society of Digital Earth (ISDE), January 21, 2025, Online.
Invited Talk, “GeoAI: Integrating geospatial data and AI for studying human-environment interactions”, Department of Geography & Geographic Information Science, University of Illinois Urbana-Champaign, November 22, 2024, Champaign, Illinois, USA.
Invited Panelist, “Promises and challenges of using large AI models in disaster response”, Specialist Meeting on Sociotechnical Foundations of GeoAI and Spatial Data Science, October 25-28, 2024, Vienna, Austria.
Invited Plenary Talk, “GeoAI for Disaster Management,” U.S. Geological Survey (USGS) Annual Research Meeting, June 25, 2024, Lakewood, CO, USA.
Invited Panelist, “GeoAI for Climate Science and Policy,” 2024 CaGIS + UCGIS Symposium, June 4, 2024, Columbus, Ohio, USA.
Invited Talk, “GeoAI Solutions for Sustainable Development: The Handbook of Geospatial Artificial Intelligence,” United Nation ITU AI for Good Webinar, February 23, 2024, Online.
Invited Talk, “Geo-knowledge-guided GPT model for extracting location descriptions from disaster-related messages,” The Federal Emergency Management Agency (FEMA), January 19, 2024, Online.
Invited Talk, “Geo-knowledge-guided GPT model for extracting location descriptions from disaster-related messages,” City of Buffalo Emergency Services, December 28, 2023, Buffalo, New York, USA.
Invited Panelist, “Human dynamics and GeoAI for enabling interdisciplinary research,” 2023 AAG Symposium on Human Dynamics Research: Status and Prospects, March 24, 2023, Denver, CO, USA.
Invited Talk, “GeoAI: Integrating Geospatial Data and AI for Studying Human-Environment Interactions,” Department of Computer Science and Engineering, University at Buffalo, October 29, 2022, Buffalo, NY, USA.
Invited Talk, “GeoAI: Integrating Geospatial Data and AI for Social Good,” Department of Geographical Sciences, University of Maryland College Park, April 21, 2022, Online.
Invited Talk, “Exploring Geo-Text Data with Machine Learning Models for Knowledge Discovery,” UB Digital Scholarship Studio & Network, April 19th, 2022, Buffalo, NY, USA.
Invited Talk, “GeoAI: Integrating Geospatial Data and AI for Social Good,” 2022 Northeast Regional Conference on Complex Systems, March 31, 2022, Buffalo, NY, USA.
Invited Interview and Conversation (hosted by Dr. Matthew Dube), “The use of social media in emergency management,” University of Maine, February 24, 2022, Online.
Invited Talk, “Aligning geographic entities from historical maps for building knowledge graphs,” the 19th International Conference on Spatial Data Handling and Geographic Intelligence, August 13, 2021, Online.
Invited Talk, “Enhancing spatial data infrastructures with artificial intelligence,” 2021 World Geospatial Developers Conference, May 19, 2021, Online.
Invited Talk, “A semantic and sentiment analysis on online neighborhood reviews,” POI Symposium at the Georgia Institute of Technology, April 30, 2021, Online.
Invited Talk, “Advancing spatial and textual analysis with GeoAI,” Peking University, April 16, 2021, Online.
Invited Talk, “Advancing spatial and textual analysis with GeoAI,” Spatial Tech Talk Series at the University of California Santa Barbara, October 21, 2020, Online.
Invited Panelist, “Integrating Space and Place in GIScience: Examples, Challenges and Opportunities,” University Consortium for Geographic Information Science, June 10, 2019, Washington, D.C., USA.
Invited Talk, “Progress and trends in geographic information systems and science,” the Singapore Program of the University at Buffalo, May 18, 2019, Singapore.
Invited Panelist, “Understanding human-place interactions through volunteered geographic information,” AAG Panel on Volunteered Geographic Information, April 4, 2019, Washington, D.C., USA.
Invited Talk, “Building benchmarking frameworks for supporting R&R: spatial and textual analysis as an example,” the Workshop on Replicability and Reproducibility (R&R) in Geospatial Research at Arizona State University, February 11-12, 2019, Tempe, AZ, USA.
Invited Talk, “Exploring geo-text data: place names, place relations, and place sentiments,” University Consortium for Geographic Information Science, January 29, 2019, Online.
Invited Talk, “Understanding place names, place relations, and place zones through geo-text data,” Oak Ridge National Laboratory, March 2, 2018, Oak Ridge, TN, USA.
Teaching
GEO 414/514: GIS and Machine Learning
GEO 414/514 is an introductory graduate course designed mostly for first-year graduate students but is
also open to qualified senior undergraduates. It starts with basic Python packages such as SciPy and Pandas,
moves on to processing vector and raster GIS data using GeoPandas and Rasterio, and then teaches students
implementing machine learning models, such as random forest and DBSCAN clustering, for applications in land
use and land cover classification and urban areas of interest detection.
GEO 503: AI for Geospatial Applications
GEO 503 is an advanced GeoAI course and focuses on teaching various deep learning and AI models, e.g., fully
connected neural networks, recurrent neural networks, and convolutional neural networks. I teach students
the use of TensorFlow and PyTorch to implement these models, and the applications of these deep learning models to
geospatial data (e.g., remote sensing images and GPS trajectory data) for problems like forecasting harmful
algal blooms and predicting future human movement locations.
GEO 281: Web-based Geographic Information Systems
GEO 281 is an entry-level GIS course for freshman and sophomore undergraduates. It introduces basic
concepts about GIS and the Web, and uses a Web-based 3D GIS platform, Google Earth Pro, with vivid 3D virtual environments to engage
students. This course aims to stimulate early interest in GIS and help students build a solid foundation for more advanced GIS courses.
GEO 481/506: Geographic Information Systems
GEO 481/506 is an advanced GIS course for junior and senior undergraduates as well as first-year graduate students.
It teaches more advanced topics, such as spatial analysis, GIS data structures, and global navigation satellite systems (GNSS),
and uses a more sophisticated GIS tool, ArcGIS Pro, to help students learn these advanced topics.