Group photo of 2025 International Graduate Student Workshop on Geo-Informatics
Prof. Wei Wan, Research Associate Professor, Institute of Remote Sensing and Geographic Information System, Peking University
Prof. Timo Balz, Professor, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
Prof. Yang YUE, Professor, Thrust of Urban Governance and Design, The Hong Kong University of Science and Technology (Guangzhou)
Over 80 attendees from 14 universities worldwide participated in 2025 International Graduate Student Workshop on Geo-Informatics
The 2025 International Graduate Student Workshop on Geo-Informatics (IGWG 2025), was successfully held on 6 December 2025, at The Hong Kong Polytechnic University (PolyU). Jointly organized by PolyU, Peking University (PKU) and Wuhan University (WHU), the workshop served as a dynamic academic platform for postgraduate students and young scholars to share cutting-edge research and foster interdisciplinary collaboration. The event drew over 80 attendees from 14 universities worldwide, facilitated a rich exchange of ideas and collaboration.
Following the Opening Ceremony, keynote speeches covering diverse areas of Geo-Informatics were delivered by:
- Prof. Wei Wan, Research Associate Professor, Institute of Remote Sensing and Geographic Information System, PKU
- Prof. Timo Balz, Professor, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, WHU
- Prof. Yang YUE, Professor, Thrust of Urban Governance and Design, The Hong Kong University of Science and Technology (Guangzhou)
- Prof. Shuo WANG, Associate Professor, Department of Land Surveying & Geo-Informatics, PolyU
The workshop featured 5 parallel sessions consisting of 30 oral presentations and 8 poster presentations, encouraging face-to-face interactions among PhD and Master's students, post-doctoral researchers and professors.
The workshop concluded with a closing address by Prof. Yang XU, Associate Head & Associate Professor, LSGI, PolyU, and the announcement of the Best Presentation Awards by Prof. Tony Chengxiang ZHUGE, Associate Professor, LSGI, PolyU, recognizing the most outstanding student presenters.
A Guided Laboratory Tour was also organized on 5 December 2025. The visitors toured 5 of the LSGI 's cutting-edge laboratories:
- Underground Utility Survey Laboratory
- Laboratory for Smart City and Spatial Big Data Analytics
- Navigation Laboratory
- Remote Sensing Laboratory
- Laboratory for Deformation and Geohazards Studies
The tour facilitated excellent idea exchange regarding the researches and technologies showcased within these advanced facilities.
Best Oral Presentation Awards (in Alphabetical Order)
|
Awardee |
Topic |
|
CHEN, Yaogang (Central South University & Universitat Politècnica de Catalunya) |
A Near-Real-Time Multi-Temporal Polarimetric InSAR Method for Landslides Monitoring in Rapid-Decorrelation Scenarios |
|
HOU, Shuyang (Wuhan University) |
Research on Key Technologies of Geospatial Code Generation for Large Language Model-Based Multi-Agent Systems |
|
LIN, Haomei (Peking University) |
Streamflow Reconstruction Using Satellite Embeddings in Highly Human Disturbed Regions |
|
PEI, Zixuan (The Hong Kong Polytechnic University) |
A High-Quality Daily Nighttime Light (HDNTL) Dataset for Global 600+ Cities (2012–2024) |
|
QI, Junnan (Peking University) |
Local Urban Development Intensity Effects on Vegetation Phenology |
|
YIN, Hanyu (Peking University) |
Multi-city Building Facade Material Dataset |
|
ZHANG, Ying (The Hong Kong Polytechnic University) |
The Potential Uptake and Climate Impacts of Hydrogen-Fuel-Cell Vehicles in Beijing |
|
ZHOU, Ning (Wuhan University) |
DepthCD: Depth Prompting in 2D Remote Sensing Imagery Change Detection |
Best Poster Presentation Awards (in Alphabetical Order)
|
Awardee |
Topic |
|
SHANG, Haibin (China University of Geosciences (Wuhan)) |
Research on the Construction and Reasoning of Flood Disaster Chain Logic Graph Based on Large Language Model |
|
ZHU, Xinrui (The Hong Kong University of Science and Technology (Guangzhou)) |
Inverse Reinforcement Learning for Elderly Travel Preference Modeling and Cognitive Map Analysis: A Case Study of Tianhe District, Guangzhou |
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