Urban Growth Modelling with Earth Observation

Session Title: 

Urban growth modelling with Earth Observation: From data-rich histories to innovative methods and scenario-based futures

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Focus:

The rapid expansion of Earth observation (EO) archives and the emergence of consistent, high-resolution global settlement datasets have transformed urban growth modelling (UGM). Data-rich EO time series now enable detailed reconstruction of past urban dynamics and provide a robust empirical basis for calibration and validation across diverse geographic contexts. This transition from data-scarce to data-rich environments is a key driver of recent methodological innovation.
Building on these data foundations, a new generation of UGM approaches is emerging. These include improved calibration strategies, spatially explicit modelling across heterogeneous regions, innovative ML/AI methods, and the integration of EO-derived datasets with complementary information sources. Such innovations enable more consistent and transferable modelling frameworks that move beyond single-case applications.
At the same time, UGM is increasingly linked to scenario-based analyses by integrating socioeconomic and climate pathways (e.g. SSP/RCP frameworks). This allows the exploration of future urban expansion under varying development trajectories and environmental conditions, supporting assessments of challenges such as climate risk, infrastructure planning, and sustainability transitions.
This session invites contributions that connect data-rich histories, methodological innovation, and scenario-based futures, with a particular focus on scalable approaches across diverse urban contexts.