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 that integrates spatial knowledge from diverse geospatial data sources, such as text, images, audio, and video. This work has the potential to significantly enhance 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_2024Sep

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 Mon Nov 18 19:14:17 2024 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 Mon Nov 18 19:14:17 2024 GMT.

Prior publications

ORCiD

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 International Journal of Applied Earth Observation and Geoinformation (doi: 10.1016/j.jag.2024.104034)(issn: 1569-8432); source: Meiliu Wu

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

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 Computers, Environment and Urban Systems (doi: 10.1016/j.compenvurbsys.2023.102008)(issn: 0198-9715); source: Meiliu Wu

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 2023 30th International Conference on Geoinformatics (doi: 10.1109/geoinformatics60313.2023.10247690); source: Meiliu Wu

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

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

Meiliu Wu, Qunying Huang, (2022) IM2City: image geo-localization via multi-modal learning Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (doi: 10.1145/3557918.3565868); source: Meiliu Wu

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

(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? Annals of GIS (doi: 10.1080/19475683.2022.2026471)(issn: 1947-5683)(issn: 1947-5691); source: Meiliu Wu

Xinyi Liu, Qunying Huang, Zhenlong Li, Meiliu Wu, (2017) The impact of MTUP to explore online trajectories for human mobility studies Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility (doi: 10.1145/3152341.3152348); source: Meiliu Wu

Supervision

Current PhD student(s):

Yuwei Cai, Building Detection in Conflict Areas with Deep Learning and Super-Resolution. 2024/08 – present.

  • 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: 

  • 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)