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Fangzheng Lyu

Assistant Professor
Portrait style shot Fangzheng Lyu
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Location:

213 Wallace Hall (0115)
295 West Campus Drive
Blacksburg, VA
24061
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Contact:

fangzheng@vt.edu
540-231-4012
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Research Gate
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Professional Website
GeoCompute Lab

My primary research interest lies in advancing GIScience, Geospatial Data Science and Computational Science to tackle complex geospatial problems and understand multi-scale urban dynamics using heterogeneous geospatial big data. Specifically, my research work focuses on 1) geospatial data science for understanding multi-scale urban dynamics, where I integrate machine learning with cyberGIS to predict and analyze complex urban phenomena (e.g. Urban Heat Islands) and develop frameworks for extracting information from heterogenous spatiotemporal data (e.g. high-frequency sensor data, social media data); 2) scalable spatial algorithms for solving complex geospatial problems, including developing scalable algorithms and models for geospatial analysis (e.g. remote sensing image fusion) using high-performance computing; and 3) democratization of data-intensive geographic research, where I innovate geospatial middleware approaches to simplify access to advanced cyberinfrastructure and enable collaborative geographic research and education.

Areas of Expertise:

  • GIS
  • CyberGIS & Geospatial Computing
  • GeoAI
  • Urban Informatics
  • Geospatial Data Science

Education:

  • Ph.D. in Geography - University of Illinois Urbana-Champaign, 2024
  • M.S. in Geography - University of Illinois Urbana-Champaign, 2021
  • B.E. in Computer Engineering - University of Hong Kong, 2018

  • Principles/Elements of GIS (GEOG 2084/5064)
  • Geospatial Tools (GEOG 2984)
  • Advanced Geospatial Data Science (GEOG 4424/5454)

  • CITY-AI: CyberTraining for AI-Enabled Intelligent Urban Informatics and Smart City Innovation 
    Sponsor: National Science Foundation: Office of Advanced Cyberinfrastructure (OAC)
    Investigator(s): Lyu, F. (PI), Michels, A. (UTD), and Song, C. (Purdue).
    Amount: $494,994
    2026 - 2029
  • AI-driven Quantification, Detection, and Analysis of Urban Retrofitting
    Sponsor: National Science Foundation ACCESS
    Investigator(s): Lyu, F. (PI)
    Amount: 200,000 SUs (Service Units)
    2025 - 2027
  • Develop Partnerships and Collaborations with GIScientists
    Sponsor: Faculty Mentoring Grant, Virginia Tech.
    Investigator(s): Lyu, F. (PI).
    Amount: $1,500
    2025–2026

  • Park, J., Lyu, F., Michels, A., Zhang, I., & Wang, S. (2026). Customized decision-making support per location's characteristics of spatial accessibility: A case study of emergency healthcare in New York City. Computers, Environment and Urban Systems, 128, 102458. https://doi.org/10.1016/j.compenvurbsys.2026.102458
  • Lyu, F., Kang, Y., & Wang, S. (2026). Converging high-performance computing and machine learning for geospatial discovery and innovation. Annals of GIS, 32(2), 195-216. https://doi.org/10.1080/19475683.2026.2639769
  • Lyu, F., Kiv, D., Ma, X., Wang, S. (2025) A Video Machine Learning Framework for Spatiotemporal Analysis of Complex Urban Dynamics. Transactions in GIS, no. 5: e70096. https://doi.org/10.1111/tgis.70096
  • Lyu, F., Yang, Z., Wang, S., Diao, C. (2025). Multi‑stream STGAN: A Spatiotemporal Image Fusion Model with Improved Spatial Transferability. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2024.3506879
  • Lyu, F., Zhou, L., Park, J., Baig, F., Wang, S. (2024). Mapping dynamic human sentiments of heat exposure with location‑ based social media data. International Journal of Geographical Information Science, 1–24. https://doi.org/10.1080/13658816.2024.2343063
  • Lyu, F., Wang, S., Han, S., Wang, S. (2022). An Integrated CyberGIS and Machine Learning Framework for Fine‑Scale Prediction of Urban Heat Island Using Satellite Remote Sensing and Urban Sensor Network Data. Urban Informatics 1, 6. https://doi.org/10.1007/s44212-022-00002-4
  • Lyu, F., Kang, JY., Wang, S., Han, S.Y., Li, Z., Wang, S. (2021). Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19. In: Shaw, SL., Sui, D. (eds) Mapping COVID-19 in Space and Time. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-72808-3_11
  • Wang, S., Lyu, F., Wang, S., Catlet, C., Padmanabhan, A., Soltani, K. (2021). Integrating CyberGIS and Urban Sensing for Reproducible Streaming Analytics. Urban Informatics, ISBN 978‑ 981‑15‑8983‑6. Springer Singapore. https://doi.org/10.1007/978-981-15-8983-6_36
  • Lyu, F., Xu, Z., Ma, X., Wang, S., Li, Z., Wang, S. (2021). A vector-based method for drainage network analysis based on LiDAR data. Computers & Geosciences, 156, 104892. https://doi.org/10.1016/j.cageo.2021.104892
  • Kang, JY., Michels, A., Lyu, F., Wang, S., Agbodo, N., Freeman, V., Wang, S. (2020). Rapidly measuring spatial accessibility of COVID‑19 healthcare resources: a case study of Illinois, USA. International Journal of Health Geographics 19, 36. https://doi.org/10.1186/s12942-020-00229-x