Dr Meiliu Wu

  • Lecturer in Geospatial Information Science (School of Geographical & Earth Sciences)

email: Meiliu.Wu@glasgow.ac.uk

The Molema Building, Lilybank Gardens, Hillhead, Glasgow, G12 8RZ

Import to contacts

ORCID iDhttps://orcid.org/0000-0002-5246-4603

Biography

I am a Lecturer in Geospatial Data Science at the University of Glasgow, starting in August 2024. I hold a Ph.D. in Geography at the University of Wisconsin-Madison (from January 2021 to May 2024). Since then, my research has been exploring innovative applications and development of Geospatial Data Science and Geospatial Artificial Intelligence (GeoAI).

My professional journey before academia includes significant industry experience as a Big Data Scientist, where I developed advanced geospatial routing algorithms, bridging the gap between theoretical research and practical implementation. I earned my Master’s degree in GIS/Cartography from UW-Madison, building on dual Bachelor’s degrees in GIS & Remote Sensing from the University of Cincinnati and Sun Yat-sen University (2+2 program).

This diverse, multidisciplinary academic and professional background has shaped my research, which is primarily focused on harnessing the power of Geospatial Data Science and GeoAI to address critical challenges in human mobility, segregation, urban analytics, and environmental and climate studies. My work is essentially driven by a strong commitment to fostering social-environmental sustainability, equity, and justice through geospatial innovations. I am particularly interested in the rapid advancements in foundation models (e.g., ChatGPT) and how these models can transform geospatial applications.

My long-term research goal is to pioneer the development of GeoAI-empowered Foundation Models (GeoFM), creating a multimodal learning framework enhanced by spatial intelligence by integrating diverse geospatial data sources, such as text, images, audio, video, and LiDAR. This work has the potential to significantly improve geospatial analysis and decision-making, ultimately contributing to more informed and equitable societal outcomes. 

I am always keen to explore collaborative opportunities and welcome inquiries from prospective Ph.D. students who are passionate about pushing the boundaries of Geospatial Data Science and GeoAI. Please feel free to contact me via meiliu.wu@glasgow.ac.uk for any collaboration, discussion of ideas, or Ph.D. student opportunities. I’m always happy to start a conversation over a coffee break!

CV: MeiliuWu_2025Feb 

Research interests

  • Geospatial Data Science, GIScience, & GeoAI
  • Urban Analytics
  • Human Mobility & Segregation
  • Social-Environmental Sustainability, Equity & Justice

Publications

List by: Type | Date

Jump to: 2023
Number of items: 1.

2023

Wu, M. , Huang, Q., Gao, S. and Zhang, Z. (2023) Mixed land use measurement and mapping with street view images and spatial context-aware prompts via zero-shot multimodal learning. International Journal of Applied Earth Observation and Geoinformation, 125, 103591. (doi: 10.1016/j.jag.2023.103591)

This list was generated on Fri Feb 21 08:59:52 2025 GMT.
Jump to: Articles
Number of items: 1.

Articles

Wu, M. , Huang, Q., Gao, S. and Zhang, Z. (2023) Mixed land use measurement and mapping with street view images and spatial context-aware prompts via zero-shot multimodal learning. International Journal of Applied Earth Observation and Geoinformation, 125, 103591. (doi: 10.1016/j.jag.2023.103591)

This list was generated on Fri Feb 21 08:59:52 2025 GMT.

Prior publications

Article

Tang Sui, Qunying Huang, Mingda Wu, Meiliu Wu, Zhou Zhang (2024) BiAU-Net: Wildfire burnt area mapping using bi-temporal Sentinel-2 imagery and U-Net with attention mechanism Meiliu Wu. ISSN 1569-8432 (doi: 10.1016/j.jag.2024.104034)

Meiliu Wu, Xinyi Liu, Yuehan Qin, Qunying Huang (2023) Revealing racial-ethnic segregation with individual experienced segregation indices based on social media data: A case study in Los Angeles-Long Beach-Anaheim Meiliu Wu. ISSN 0198-9715 (doi: 10.1016/j.compenvurbsys.2023.102008)

Jirapa Vongkusolkit, Bo Peng, Meiliu Wu, Qunying Huang, Christian G. Andresen (2023) Near Real-Time Flood Mapping with Weakly Supervised Machine Learning Meiliu Wu. ISSN 2072-4292 (doi: 10.3390/rs15133263)

Meiliu Wu, Qunying Huang, Song Gao (2022) Mixed Land Use Detection via Vision-Language Multi-modal Learning Crossref. (doi: 10.22541/essoar.167252584.47036748/v1)

Xinyi Liu, Meiliu Wu, Bo Peng, Qunying Huang (2022) Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data Meiliu Wu. ISSN 2045-2322 (doi: 10.1038/s41598-022-19441-9)

(2022) Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? Meiliu Wu. ISSN 1947-5691 (doi: 10.1080/19475683.2022.2026471)

Conference Proceedings

Mingda Wu, Qunying Huang, Tang Sui, Meiliu Wu (2023) Pixel-wise Wildfire Burn Severity Classification with Bi-temporal Sentinel-2 Data and Deep Learning Crossref. (doi: 10.1145/3627377.3627433)

Meiliu Wu, Qunying Huang, Song Gao (2023) Measuring Access Inequality in A Hybrid Physical-Virtual World: : A Case Study of Racial Disparity of Healthcare Access During CoVID-19 Meiliu Wu. (doi: 10.1109/geoinformatics60313.2023.10247690)

Meiliu Wu, Qunying Huang (2022) IM2City: image geo-localization via multi-modal learning Meiliu Wu. (doi: 10.1145/3557918.3565868)

Xinyi Liu, Qunying Huang, Zhenlong Li, Meiliu Wu (2017) The impact of MTUP to explore online trajectories for human mobility studies Meiliu Wu. (doi: 10.1145/3152341.3152348)

Grants

  • 2025 UKRI Natural Environment Research Council (NERC) GALLANT Innovation Fund on GOTREES (Govan Tree-community for ecosystem services) (£48,000, co-PI, under review)
  • 2024-25 University of Glasgow Early Career Mobility Scheme Award (£5,000, PI)
  • 2024-26 University of Glasgow College of Science and Engineering Start-up Grant on GeoAI Multimodal Learning Foundation Models (£5,000, PI)

Supervision

  • Cai, Yuwei
    Enhancing Building Footprint Extraction Accuracy Using Single-Image Super-Resolution Building Datasets
  • HE, ZHIMENG
    AI-based Extraction of Building Rooftops to Support Indigenous Community Planning in Canadian North

Teaching

Current: 

  • Program Convenor of planned MSc in Geospatial Data Science and AI, F26
  • GEOG5018 Principles of Cartographic Design & Production, F24 (In-person; 40 graduates);

Previous:

  • Geog 574 Geospatial Database Design and Development, F23 & F17 (In-person; 50 graduates & undergraduates); S19 (Online; 50 graduates from UW-Madison GIS Professional Programs)
  • Geog 170 Intro of GIScience and its Technology, S21 & S24 (Online; 485 undergraduates)
  • Geog 576 Web Interactive Mapping & Geovisualization, S19 (Online; 50 graduates from UW-Madison GIS Professional Programs)
  • Geog 370 Introduction to Cartography, F18 (In-person; 80 graduates & undergraduates)
  • Geog 578 GIS Applications, S18 (In-person; 50 graduates & undergraduates)