Special session 12:
Geospatial artificial intelligence (GeoAI) in urban planning
Organisers: MichaÅ‚ Rzeszewski (Adam Mickiewicz University), Phil Jones (University of Birmingham), Maciej GÅ‚ówczyÅ„ski (Adam Mickiewicz University), Tess Osborne (University of Leicester), Stef De Sabbata (3), and Adam Wronkowski (Adam Mickiewicz University)​
​Contact: macglo@amu.edu.pl
Geospatial artificial intelligence (GeoAI) combines AI, GIS and high-performance computing to tackle spatial issues (Janowicz et al., 2022). Therefore, it can influence urban planning on many different levels. Amidst many applications, they could range from machine learning analytics through pattern recognition and urban simulation modelling (Liu et al., 2022; Liu et al., 2024). Above all, GeoAI has led to advancements in traditional techniques and methods for planning and analysing urban spaces. GeoAI allows urban planners and professionals to look in-depth into the complexities of urban data, city dynamics and socio-spatial patterns. It also supports evidence-based decision-making by gaining deeper urban space analytics. Moreover, generative AI visualisation and representation capabilities have brought us to a moment where it may influence the transformation of urban space perceptions, design and place imaginaries (Kim et al., 2022; Chang et al., 2023; Guridi et al., 2024).
In this session we will focus on exploring the role of GeoAI in addressing contemporary urban challenges and advancing current planning approaches. We invite researchers and practitioners working at the intersection of AI, geospatial analysis and urban planning to participate in this session. A range of theoretical and empirical research are welcome, including case studies, methodological advances and critical perspectives.
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Potential topics cover:
(1) Geo AI spatial analysis and urban modelling;
(2) GeoAI application in smart cities;
(3) GeoAI and big data;
(4) Open data in AI-based urban planning;
(5) Applications of generative AI in urban planning;
(6) Ethical challenges in AI-based urban decision-making;
(7) Bias in GeoAI.