Doctor of Philosophy
Aug. 2024 – PresentTexas A&M University
College Station, USA
About
Hi, I'm Wenyu 👋
I am a PhD student working on GIS-based urban analysis, with a strong interest in using spatial data to understand how cities function through a human-centered and empathetic lens. My research applies geospatial analytical methods to key urban challenges, with a particular focus on informal settlements (slums) mapping, human mobility during disasters, and the interactions between people and the built environment.
Beyond academia, I enjoy travel and photography. I have visited ten countries across Asia, Oceania, North America, and Europe, and my next destination is Africa, where I hope to continue broadening my understanding of cities, cultures, and societies.
Education
Master of Science
Aug. 2023 – Sep. 2024National University of Singapore
Singapore
Bachelor of Science
Sep. 2019 – Jun. 2023Central China Normal University
Wuhan, China
Publications (total 9 Publications) · Google Scholar
Selected Publications
Zhang, W., Jamonnak, S., Bai, W., & Ye, X. (2026). Bridging text and topology: empowering large language models through graph retrieval for constraint-aware reasoning in campus digital twins. International Journal of Digital Earth, 19(1).
Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating human language from heterogeneous data sources. However, their integration with geospatial information remains limited, particularly in complex and dynamic environments where spatial reasoning must satisfy explicit physical, temporal, and operational constraints. This paper presents a constraint-aware Graph-based Retrieval-Augmented Generation (GraphRAG) approach for spatial reasoning in campus digital twin settings, designed to bridge unstructured text with geospatial topology. The framework integrates LLMs with a spatiotemporal knowledge graph that explicitly models campus facilities, events, accessible entrances, and real-time parking availability. Unlike traditional vector-based retrieval approaches, it leverages graph-structured retrieval to resolve critical constraints during the retrieval stage, thereby restricting downstream language generation to feasible and executable spatial configurations. This enables accurate responses to multi-criteria spatial queries, such as route planning and parking recommendations under multiple spatial and operational conditions. The framework is implemented as a constraint-aware spatial decision-support chatbot and integrated with an interactive visual analytics interface, allowing users to visually inspect the system's reasoning process and outcomes. Experimental results demonstrate that the proposed GraphRAG approach significantly outperforms baseline retrieval-augmented generation methods in both accuracy and interpretability.
Zhang, W., Tong, Y., Liu, Y., & Cao, R. (2025). Mapping the vanishing and transformation of urban villages in China. Sustainable Cities and Society, 106970.
Urban villages (UVs), informal settlements embedded within China's urban fabric, have undergone widespread demolition and redevelopment in recent decades. However, there remains a lack of systematic evaluation of whether the demolished land has been effectively reused, raising concerns about the efficacy and sustainability of current redevelopment practices. To address the gap, this study proposes a deep learning-based framework to monitor the spatiotemporal changes of UVs in China. Specifically, semantic segmentation of multi-temporal remote sensing imagery is first used to map evolving UV boundaries, and then post-demolition land use is classified into six categories based on the "remained–demolished–redeveloped" phase: incomplete demolition, vacant land, construction sites, buildings, green spaces, and others. Four representative cities from China's four economic regions were selected as the study areas, i.e., Guangzhou (East), Zhengzhou (Central), Xi'an (West), and Harbin (Northeast). The results indicate: (1) UV redevelopment processes were frequently prolonged; (2) redevelopment transitions primarily occurred in peripheral areas, whereas urban cores remained relatively stable; and (3) three spatiotemporal transformation pathways, i.e., synchronized redevelopment, delayed redevelopment, and gradual optimization, were revealed. This study highlights the fragmented, complex and nonlinear nature of UV redevelopment, underscoring the need for tiered and context-sensitive planning strategies.
Cao, R., Tu, W., Chen, D., & Zhang, W. (2025). Mapping urban villages in China: Progress and challenges. Computers, Environment and Urban Systems, 119, 102282.
The shift toward high-quality urbanization has brought increased attention to the issue of "urban villages", which has become a prominent social problem in China. However, there is a lack of available geospatial data on urban villages, making it crucial to prioritize urban village mapping. In order to assess the current progress in urban village mapping and identify challenges and future directions, we have conducted a comprehensive review, which to the best of our knowledge is the first of its kind in this field. Our review begins by providing a clear context for urban villages and elaborating the method for literature review, then summarizes the study areas, data sources, and approaches used for urban village mapping in China. We also address the challenges and future directions for further research. Through thorough investigation, we find that current studies only cover very limited study areas and periods and lack sufficient investigation into the scalability, transferability, and interpretability of identification approaches due to the challenges in concept fuzziness and variances, spatial heterogeneity and variances of urban villages, and data availability.
Zhao, F., Dai, Z., Zhang, W., Shan, Y., & Fu, C. (2023). Epidemiological-survey-based multidimensional modeling for understanding daily mobility during the COVID-19 pandemic across urban-rural gradient in the Chinese mainland. Geo-Spatial Information Science, 26(4), 603–615.
Human mobility survey data usually suffer from a lack of resources for validation. Epidemiological survey records, which are released to the public as a containment measure by local authorities, provide place visitation details validated by the authority. This study collected and analyzed the epidemiological survey reports published by local governments in the Chinese mainland, between January 2020 and November 2021. To reveal the mobility patterns during the COVID-19 pandemic across the urban-rural gradient in China's mainland, we derived key mobility indicators from the epidemiological survey data from rural to megacities. We then applied exploratory factor analysis to identify latent factors that affected people's mobility. We found that the pandemic poses varying impacts across the urban-rural gradient in the Chinese mainland, and the mobility patterns of middle and small cities are more influenced. Our results also showed that the pandemic did not enlarge gender gap in people's mobility, as gender was not a significant driving factor for explaining people's quantity of out-of-home activities as well as extent of life space, while age group and city levels were significant.
Teaching
Spring 2026
Instructor of Record
URPN 325: GIS in Landscape & Urban Planning, Texas A&M University.
Fall 2025
Instructor of Record
PLAN 625 & URPN 325: GIS in Landscape & Urban Planning, Texas A&M University.
Student Evaluations — Fall 2025 TAMU End-of-Term Survey
- Effective teacher4.67 / 5
- Willing to help students4.93 / 5
- Learning environment4.80 / 5
"She was genuinely so involved and dedicated to helping us. … she proactively reached out and helped me through my confusion with assignments."
"Very knowledgeable and open to helping."
"The Canvas was set up very well and everything was always very easy to access."
"Wenyu was such a great TA and was always willing to help us understand!"
Projects
01
GeoAI for Urban Transformation & Geospatial Annotation
Measuring the disappearance, redevelopment, and functional transformation of urban spaces — with a focus on urban villages in China — through high-resolution remote sensing, image segmentation, and spatial data integration. This includes advancing methods for annotating and interpreting complex urban targets in remotely sensed imagery, supporting scalable geospatial intelligence pipelines for urban governance and planning applications.
Related: [4] [6] [7] · Under Review: Geospatial data for urban villages
02
Digital Twins and Spatial Reasoning
Developing knowledge-aware and graph-based retrieval systems for urban digital twins, enabling constraint-aware spatial reasoning, interpretable decision support, and natural-language querying of geographic entities.
Related: [5] [10]
03
Climate Adaptation and Mobility Behavior
Understanding how heat exposure and hazard contexts reshape urban behavior and mobility patterns, using GeoAI to assess where and how adaptation emerges spatially across different urban environments.
Related: Working paper (Shops, Shelters, and Survival)
Awards
2026
TAMIDS Data Science Student Ambassador Award $2,000
Texas A&M Institute of Data Science (TAMIDS)
GeoSAT Student Fellowship Program, Communications Fellow $500
GeoSAT, Texas A&M University
Research and Presentation Travel Award $1,000
Graduate & Professional School, Texas A&M University
Howard Balanoff Scholarship $1,000
Texas A&M University
Graduate Student Affinity Group Research and Support Award $250
American Association of Geographers (AAG) · Winner (5/150)
International Geographic Information Funds Scholarship Award $1,200
American Association of Geographers (AAG)
Jeanne X. Kasperson Student Paper Competition $220
American Association of Geographers (AAG)
2025
College of Architecture Travel Grant $600
Texas A&M University (2025 & 2026)
CISG Robert Raskin Student Paper Competition — Finalist
American Association of Geographers (AAG)
2024
Conference Travel Grant $250
International Cartographic Association (ICA) · AsiaCarto Conference
PhD Admission with Full Funding — Graduate Research Assistantship (4 Years)
Texas A&M University
2022
First Prize, "TRMSG·ISA Cup" Geography Teaching and Mapping Competition
National · China
2021
Grand Prize, National University GIS Application Skills Competition
National · China
Second Prize, China University Computer Design Competition (Web Application)
National · China
Winning Prize, ESRI Cup GIS Software Development Competition
National · China
Service
Academic Service
2026
Student Leadership Committee Co-chair
Symposium on Human Dynamics Research, AAG 2026, San Francisco.
2026
Session Organizer
Mapping Informal Settlements: Geospatial Intelligence for Equitable Urban Transformation, AAG 2026.
2025
Session Organizer
Shrinking Cities: Effects, Spatial Challenges, and Response Strategies, AAG 2025, Detroit.
Peer Review
- Cities
- Climate Risk Management
- Computers, Environment and Urban Systems
- Environment and Planning B: Urban Analytics and City Science
- Humanities and Social Sciences Communications
- Journal of Asian Architecture and Building Engineering
- npj Natural Hazards
- Asian Geographer
Skills
- Python
- R
- ArcGIS Pro
- QGIS
- Google Earth Engine
- Large Language Models
- WebGIS
Footprint
Academic institution Conference
Academic Institutions
- 🇺🇸Texas A&M UniversityCollege Station, Texas · 2024–present
- 🇸🇬National University of SingaporeSingapore · 2023–2024
- 🇭🇰Hong Kong Univ. of Science and TechnologyGuangzhou, China · 2024
- 🇨🇳Central China Normal UniversityWuhan, China · 2019–2023
- 🇨🇳Aerospace Information Research Institute, CASBeijing, China · 2022 (Internship)
Conferences & Presentations
- 🇺🇸AAG Annual Meeting 2026San Francisco, California
- 🇫🇮WPSC 2026Helsinki/Espoo, Finland · July 2026
- 🇨🇦International Cartographic Conference 2025Vancouver, British Columbia
- 🇺🇸AAG Annual Meeting 2025Detroit, Michigan
- 🇭🇰Asian Cartographic ConferenceHong Kong
- 🇺🇸Symposium on Spatiotemporal Data ScienceArlington, Virginia
Contact
For research collaborations, conference invitations, and academic opportunities, please reach out by email.