Suiyuan Wang

Suiyuan Wang

Ph.D. Candidate in Geography, University at Buffalo

Ph.D. Candidate · Geography · University at Buffalo

Suiyuan Wang

My research combines remote sensing, geospatial AI, and spatiotemporal analysis to measure urban change and inform sustainable neighborhood development.

I am a Ph.D. candidate in Geography at the University at Buffalo, advised by Dr. Le Wang. My dissertation examines high spatiotemporal resolution impervious-surface change using ensemble remote sensing foundation models. My broader interests include fine-resolution population estimation and the social and environmental dimensions of urban change.

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Research highlights

Monitoring urban change with remote sensing

How can we monitor changes in urban impervious surfaces at fine spatial and temporal scales? My work uses PlanetScope image time series to quantify change, alongside ongoing research on ensemble remote sensing foundation models for nationwide U.S. mapping.

  1. 01ObserveSatellite imagery across space and time
  2. 02AnalyzeTime-series analysis and remote sensing models
  3. 03UnderstandImpervious-surface change and urban development
Conceptual overview of my urban-change research: satellite observations support analysis of impervious surfaces and their changes.

Read the 2026 PlanetScope paper · Related funded projects

Mapping fine-resolution populations

How can population maps reflect changes within neighborhoods over time? In work with Le Wang, I combine remote sensing, mobile-phone data, and land-use information to estimate monthly populations at the census-block level.

Two parallel modeling steps estimate monthly census-block-group populations and block-level weights; dasymetric allocation combines them to estimate monthly census-block populations.
Population-mapping framework from Wang & Wang (2024). Monthly population estimates for census block groups are combined with block-level weights to produce finer-resolution population maps. Select the figure to view it at full size.

Read the open-access paper

Publications

Peer-reviewed articles

  1. Wang, S., & Wang, L. (2026). Quantifying High Spatiotemporal Impervious Surface Change with Time Series PlanetScope Imagery. Remote Sensing Applications: Society and Environment, 41, 101890.
  2. Yin, L., Silverman, R. M., Wang, S., Patterson, K. L., & Rehman-Veal, A. (2026). Using ChatGPT to enhance public participation: an analysis of public comments to reschedule marijuana. Transforming Government: People, Process and Policy, 1–16.
  3. Yin, L., Wang, S., Patterson, K. L., Silverman, R. M., & Rehman-Veal, A. (2024). Marijuana Dispensary Locations and Neighborhood Characteristics in New York City. ISPRS International Journal of Geo-Information, 14(1), 4.
  4. Wang, S., & Wang, L. (2024). A Novel Framework for Mapping Updated Fine-resolution Populations with Remote Sensing and Mobile Phone Data. Journal of Remote Sensing, 4, 0227.
  5. Patterson, K. L., Silverman, R. M., Rehman-Veal, A., Yin, L., & Wang, S. (2024). Building the Prison to Legal Drug Dealing Pipeline: A Comparative Analysis of Social Equity Policies in Recreational Cannabis Licensing. Societies, 14(6), 88.
  6. L’Huillier, J. C., Boccardo, J. D., Stewart, M., Wang, S., Myneni, A. A., Bari, A. A., … & Noyes, K. (2024). Gun violence revictimization in New York State: What increases the risk of being shot again? Journal of Trauma and Acute Care Surgery, 97(4), 604–613.

Book chapter

Gibbes, C., Wang, S., Yin, F., Dao, T. H. D., & Vogt, B. (2026). Geovisualization and Remote Sensing: Bridging Complexity and Interpretation (Chapter 14). In E. Bunting, J. Southworth, C. Gibbes, & H. Herrero (Eds.), Remote Sensing, Big Data, and GeoAI: Exploring Applications with Geospatial Insights. Elsevier.

Work in progress

  • Liu, W., Yin, F., & Wang, S. Evaluating GPT Models for Urban Environmental and Perceptual Attribute Labeling from Street View Images. In progress.
  • Wang, S., & Wang, L. (2026). Nationwide U.S. very-high-resolution Impervious Surfaces Maps 2022–2024 with Ensemble Remote Sensing Foundation Models. In preparation for Remote Sensing of Environment, special issue on geospatial foundation models.
  • Yin, L., Wang, S., Patterson, K. L., Silverman, R. M., & Rehman-Veal, A. (2025). Exploring Media Perspectives and Public Sentiments of New York State Recreational Marijuana Legalization: Analyzing New York Times and Twitter Data using ChatGPT. In submission.

Teaching experience

As solo instructor for GEO 120 Maps: Earth from Above in Spring 2025, I taught 97 students and designed weekly lectures and assignments. My lab teaching spans GIS, cartography, environmental modeling, and remote sensing, including updating course materials from ArcMap to ArcGIS Pro.

GEO 120 syllabus

  • GEO 381 Cartography — Lab instructor, Spring 2026, 2025, 2023, and 2022.
  • GEO 479/559 GIS for Environmental Modeling — Lab instructor, Spring 2023.
  • GEO 483/553 Remote Sensing — Lab instructor, Fall 2022.
  • GEO 481/506 Introduction to Geographical Information Systems — Lab instructor, Fall 2021.
  • Guest lectures — Introduction to Remote Sensing, University of Pattimura webinar (May 2024); Introduction to Drone and Demo Flying, GEO 381 (April 2024).

Recent news

  • 2026 · Published research on impervious-surface change using PlanetScope imagery, and coauthored an article on ChatGPT and public participation and a chapter on geovisualization and remote sensing.
  • March 2026 · Presented impervious-surface change research and chaired a session on heat exposure, human comfort, and urban land cover at AAG.
  • January 2026 · Received a $1,000 Google Cloud research grant as PI for foundation AI-driven U.S. impervious-surface mapping.
  • December 2025 · Co-PI on a $71,295 SUNYAI Platform project connecting remote sensing foundation models with urban scaling and peer-city discovery.

Education

  • Ph.D. in Geography, candidate, University at Buffalo, 2020–present.
    Advisor: Dr. Le Wang. Committee: Dr. Andrew Crooks and Dr. Jessie P. Poon.
    Dissertation: Monitoring High Spatiotemporal Resolution Impervious Surface Change Using Ensemble Remote Sensing Foundation Models.
  • M.S. in General Science, East Stroudsburg University, 2019.
  • B.S. in Geography, Henan University, 2019.

Funded projects

  • Google Cloud research grant · January 2026–January 2027 · $1,000 · PI: Suiyuan Wang.
    Foundation AI-Driven 0.6 m U.S. Impervious Surface Mapping.
  • SUNYAI Platform · December 2025–March 2026 · $71,295 · Award #2025-32 · PI: Le Wang; Co-PI: Suiyuan Wang.
    Decoding Urban Scaling Theory with AI Foundation Models and Remote Sensing: U.S. 0.3–2 m Impervious Surface Mapping (2002–2024) and Peer City Discovery.

Professional appointments

  • Spring 2026 · Teaching Assistant, Department of Geography, University at Buffalo.
  • Spring 2025 · Adjunct Instructor, Department of Geography, University at Buffalo.
  • 2024–2025 · Research Assistant, Department of Urban and Regional Planning, University at Buffalo.
  • 2023–2024 · Research Assistant, Center for Urban Studies, University at Buffalo.
  • 2021–2023 · Teaching Assistant, Department of Geography, University at Buffalo.

Selected honors and awards

  • 2026 · Graduate School Conference Travel Grant, University at Buffalo.
  • 2024, 2025 · AAG Student Travel Grants.
  • 2024 · First place, Student Honors Paper Competition, AAG Remote Sensing Specialty Group.
  • 2024 · Third place, Student Paper Competition, Middle States AAG.
  • 2024 · State of the Map US Scholarship.
  • 2021–2025 · Graduate School Conference Travel Grants, University at Buffalo.
  • 2019 · Excellent Student Award, Henan University.
  • 2017 · Provincial Outstanding Volunteer Team Award, China Land Science Society funded village land-use planning project, Changyuan County.

Selected presentations

  • 2026 · AAG, San Francisco. Quantifying High Spatiotemporal Impervious Surface Change with Time Series PlanetScope Imagery. With Le Wang.
  • 2024 · AAG, Honolulu. Continuous monitoring of high spatial and temporal resolution impervious surfaces based on PlanetScope time series. With Le Wang.
  • 2024 · State of the Map US, Salt Lake City. Watch the recorded talk.
  • 2023 · AAG, Denver. A novel framework for mapping updated fine-resolution populations with remote sensing and mobile phone data. With Le Wang.
  • 2022 · AAG, virtual. Mapping fine-scale population distribution at the building level using improved geographical random forest with multi-source data. With Le Wang.

Affiliations and service

  • 2026 · Session Chair, Spatiotemporal Analysis of Heat Exposure, Human Comfort, and Urban Land Cover, AAG.
  • 2024–2026 · Vice President, Geography Graduate Student Associate, University at Buffalo.
  • 2023–2025 · Student Co-director, Remote Sensing Specialty Group, AAG.
  • 2023 · Committee member, Graduate Student Employees Union Spring Contract Bargaining Committee.
  • Reviewer · International Journal of Remote Sensing; Mark Diamond Research Foundation Grant, University at Buffalo.