Call for abstracts
Over the past decade, significant advancements in deep learning have launched a new “AI spring,” reigniting research interest in artificial intelligence within GIScience and quantitative geography. While large language models have been making headlines in newspapers worldwide, a broader range of foundation models and architectures (e.g., CNN, GNN GAN, LSTM, Transformer) have sparked new work in GeoAI (Janowicz et al., 2020; De Sabbata et al., 2023, Hu et al. 2024, Mai et al., 2025). For example, vision models have been applied to street-view imagery to explore urban perceptions and infer socio-economic outcomes (Biljecki and Ito 2021; Law et al 2019). The transformer architecture has been used for natural language processing (Berragan et al., 2023), image analysis and machine vision (Li et al, 2022). Graph neural networks have been employed for geodemographic classifications and question-answering with geographic knowledge graphs (De Sabbata and Liu, 2023; Mai et al, 2020). More recently, Geo foundation models have emerged, leveraging large-scale geographic data to support multi-modal geographic analysis.
This session aims to be a forum to discuss advances, opportunities, and challenges of the use of GeoAI in quantitative geography and geographic information science, showcasing the latest advancements in GeoAI theories, methods and applications within the realm of quantitative geographic studies. We invite submissions that engage with the research agenda recently proposed by Nelson et al. (2024) and focus on but not limited to the topics below:
- GeoAI methods, including but not limited to
- Spatial Explicit Machine learning
- Causal inference
- Uncertainity
- Agent-based modelling
- Machine Vision
- Natural language processing
- Information retrieval
- Foundation models
- Explainable and interpretable AI
- Reinforcement learning
- Generative AI
- GeoAI applications, including but not limited to
- Crowdsourcing, citizen science and volunteered geographic information
- Data integration
- Environment (Disaster Management and Resilience)
- Health
- Location-based services
- Mapping, cartography and information visualisation
- Spatial analysis and uncertainty
- Transportation & Mobility
- Urban analytics
- Urban Planning
- Critical GIS, ethics and privacy in GeoAI
- Bias and fairness
- Responsible AI
- Policy implications
- Transparency and accountability
- Reproducibility and open science in GeoAI
- Open Data for reproducibility and transparency
- Standardising benchmarks and evaluation metrics
Submssion
Please submit your 400-word abstract to Stef De Sabbata by March 3rd.
Organisers
- Stef De Sabbata, University of Leicester
- Stephen Law, University College London
- Xiao Li, University of Oxford
- Francisco Rowe, University of Liverpool
- Trivik Verma, University of Bristol
- Godwin Yeboah, University of Warwick
- Qunshan Zhao, University of Glasgow
- Rui Zhu, University of Bristol
References
- Berragan, C., Singleton, A., Calafiore, A., & Morley, J. (2023). Transformer based named entity recognition for place name extraction from unstructured text. International Journal of Geographical Information Science, 37(4), 747-766.
- De Sabbata, S., Ballatore, A., Miller, H.J., Sieber, R., Tyukin, I. and Yeboah, G., 2023. GeoAI in urban analytics. International Journal of Geographical Information Science, 37(12), pp.2455-2463.
- De Sabbata, S., & Liu, P. (2023). A graph neural network framework for spatial geodemographic classification. International Journal of Geographical Information Science, 37(12), 2464-2486.
- Hu, Yingjie, Michael Goodchild, A.-Xing Zhu, May Yuan, Orhun Aydin, Budhendra Bhaduri, Song Gao, Wenwen Li, Dalton Lunga, and Shawn Newsam. 2024. ‘A Five-Year Milestone: Reflections on Advances and Limitations in GeoAI Research’. Annals of GIS, 0(0):1–14. doi: 10.1080/19475683.2024.2309866.
- Janowicz, K., Gao, S., McKenzie, G., Hu, Y. and Bhaduri, B., 2020. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), pp.625-636.
- Law, S., Paige, B., & Russell, C. (2019). Take a look around: using street view and satellite images to estimate house prices. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), 1-19.
- Li, W., & Hsu, C. Y. (2022). GeoAI for large-scale image analysis and machine vision: Recent progress of artificial intelligence in geography. ISPRS International Journal of Geo-Information, 11(7),
- Mai, G., Janowicz, K., Cai, L., Zhu, R., Regalia, B., Yan, B., Shi, M. and Lao, N., 2020. SE‐KGE: A location‐aware knowledge graph embedding model for geographic question answering and spatial semantic lifting. Transactions in GIS, 24(3), pp.623-655.
- Mai, G., Xie, Y., Jia, X., Lao, N., Rao, J., Zhu, Q., ... & Jiao, J. (2025). Towards the next generation of Geospatial Artificial Intelligence. International Journal of Applied Earth Observation and Geoinformation, 136, 104368.
- Nelson, T., Frazier, A. E., Kedron, P., Dodge, S., Zhao, B., Goodchild, M., ... & Wilson, J. (2024). A research agenda for GIScience in a time of disruptions. International Journal of Geographical Information Science, 1-24.
- Biljecki, F., & Ito, K. (2021). Street view imagery in urban analytics and GIS: A review. Landscape and Urban Planning, 215, 104217.