Dr Weiming Huang

Profile

I am a Lecturer in Urban Data Science at the School of Geography and a member of the Institute for Spatial Data Science. Before joining Leeds in 2024, I had a blended background in Geographical Information Science (GIScience) and Computer Science. I obtained my PhD in GIScience from Lund University, Sweden, and was a Wallenberg Postdoctoral Fellow at Nanyang Technological University, Singapore, and Lund University, primarily in a Computer Science environment.
My current research focuses on developing and leveraging geospatial foundation models. This direction is a convergence of my long-standing expertise in spatial data mining and the transformative potential of foundational AI models. The goal is to unlock deeper insights from multi-modal geospatial data, including points of interest, remote sensing/street view imagery, and human trajectories, for applications ranging from sustainable urban planning to critical environmental monitoring.
My research has resulted in over 50 articles in leading GIScience journals and top-tier Computer Science conferences. I have also served as a guest editor for IJGIS, TGIS, JAG, and the Semantic Web journal, and have acted as a programme committee member for a number of prestigious machine learning/data mining conferences, including NeurIPS, ICLR, and KDD. I am also a Fellow of the Royal Geographical Society.
Major Grants & Awards
• Waldo Tobler Young Researcher Award from Austrian Academy of Sciences, 2022
• EuroSDR Award for the Best PhD Thesis Related to Geoinformation Science, 2021
• Wallenberg Postdoctoral Fellowship at Nanyang Technological University and Lund University, 2020

Research interests

My research leverages both top-down, theory-informed approaches and bottom-up, data-driven methods (the two pathways of GeoAI), to deepen our understanding of the complex urban and natural environments, and finally inform decision-making towards sustainable development. The full list of my publications can be found here. My research can be broadly grouped into three main research themes:
Geospatial foundation models: The recent surge of foundation models, e.g., GPT-family models, has catalysed a new frontier in GeoAI research. We are at the forefront of this movement, contributing to foundational debates on their utility (Mai et al. 2024) and the principles for developing geospatial foundation models (Janowicz et al. 2025). Pioneering this direction, we developed some of the first dedicated geospatial foundation models. For example, our City Foundation Model (CityFM) jointly learns representations of various urban entities from OpenStreetMap, enhancing performance across a range of urban analyses (Balsebre et al. 2024). Similarly, we developed Garner, a geospatial foundation model for road-level tasks, such as average speed prediction (Zhou et al. 2024). A parallel research thrust explores the adaptation of existing vision-language models for geospatial tasks. We proposed the UrbanCLIP framework to enable the pretrained vision-language model CLIP to effectively infer fine-grained urban land use in a zero-shot manner, i.e. with no additional model training or labelled samples (Huang et al. 2024).
Spatial data mining for urban analytics: The growing proliferation of urban sensing data sources calls for effective data mining methods to extract useful insights from vast amounts of data. In this context, our research seeks to learn general-purpose and effective representations (vector embeddings) from a variety of urban sensing data. These representations then serve as effective features to enhance the performance of various urban analyses. We have developed novel representation learning methods using a wide array of data modalities, e.g., points of interest (POIs) (Huang et al. 2023), OpenStreetMap (Li et al. 2023), street view images (Zhou et al. 2024), remote sensing data (Bai et al. 2023), and human trajectories (Zhang et al. 2024). These studies have demonstrated the effectiveness of representation learning in urban analytics, such as inferring functional land use, population density, traffic speed, house prices, individual next locations, and crime rates. Pushing the boundaries of this direction, we also investigated the transferability of these learned representations across multiple cities (Chen et al. 2025)
Geospatial knowledge graphs: Our research in this direction explores the use of knowledge graphs for two main purposes: 1) integrating multi-source geospatial data, and 2) formalising geovisualisation knowledge to enhance its interpretation, transfer, and reuse. In this vein, I have authored the Geospatial Knowledge Graph entry in the GIS&T Body of Knowledge, and our benchmarking work has been used by European Spatial Data Research (EuroSDR). We have also developed several knowledge bases for geovisualisation, e.g. for heritage building and natural reserve analyses (Huang et al. 2017, 2020).

Student education

I presently teach several subjects within the Data Science sector.
 

Research groups and institutes

• Institute for Spatial Data Science