The interactions between geo-environmental and anthropic processes are increasing due to the ever-growing population and its related side effects (e.g., urban sprawl, land degradation, natural resource and energy consumption, etc.). Natural hazards, land degradation and environmental pollution are three of the possible “interactions” between geosphere and anthroposphere. In this context, spatial and spatiotemporal data are of crucial importance for the identification, analysis and modelling of the processes of interest in Earth and Soil Sciences. The information content of such geo-environmental data requires advanced mathematical, statistical and geomorphometric methodologies in order to be fully exploited.

The session aims to explore the challenges and potentialities of quantitative spatial data analysis and modelling in the context of Earth and Soil Sciences, with a special focus on geo-environmental challenges. Studies implementing intuitive and applied mathematical/numerical approaches and highlighting their key potentialities and limitations are particularly sought after. A special attention is paid to spatial uncertainty evaluation and its possible reduction, and to alternative techniques of representation of spatial data (e.g., visualization, sonification, haptic devices, etc.).

In the session, two main topics will be covered (although the session is not limited to them!):
1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty: geostatistics, machine learning, statistical learning, etc.
2) Analysis and representation of exhaustive spatial data at different scales and resolutions: geomorphometry, image analysis, machine learning, pattern recognition, etc.