Fangzheng Lyu
Fangzheng Lyu
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Location:
213 Wallace Hall (0115)295 West Campus Drive
Blacksburg, VA
24061
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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
- Spatial AI
- Computational and Data Science
- Urban Informatics
- CyberGIS & Geospatial Computing
Education:
- Ph.D. - University of Illinois Urbana-Champaign, 2024
- M.S. - University of Illinois Urbana-Champaign, 2021
- B.E. - University of Hong Kong, 2018
- Principles of GIS (GEOG 2084)
- Geospatial Tools (GEOG 2984)
- Geospatial Data Science (GEOG 4984)
- Elements of GIS (GEOG 5064 )
- Spatial Data Science (GEOG 5984)
CIS250619: AI‑Driven Quantification, Detection, and Analysis of Urban Retrofitting. 2025. NSF ACCESS. (PI)
- 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
- Ma, X., Song, Y., Lyu, F., Yang, Y., Wang, Y., Li, X., & Zhong, S. (2025). Revitalizing Cities: Growth and Change, 56(1), e70018.
https://doi.org/10.1111/grow.70018
- Kang, Y., Lyu, F., & Wang, S. (2024). NetPointLib: Library for Large-Scale Spatial Network Point Data Fusion and Analysis. In Practice and Experience in Advanced Research Computing 2024: Human Powered Computing (pp. 1-4).
https://doi.org/10.1145/3626203.3670615 - Song, Z., Zhang, Z., Lyu, F., Bishop, M., Liu, J., & Chi, Z. (2024). From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale Disease Spread. Sustainability, 16(12), 5036.
https://doi.org/10.3390/su16125036 - 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 Sinapore.
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 - Lyu, F., et al. (2019). Reproducible hydrological modeling with CyberGIS-Jupyter: a case study on SUMMA. In Proceedings of the practice and experience in advanced research computing on rise of the machines (learning) (pp. 1-6).
https://doi.org/10.1145/3332186.3333052