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Learning from spatial data: unveiling the geo-environment through quantitative approaches (co-organized)
Convener: Sebastiano Trevisani  | Co-Conveners: Igor Bogunović , Marco Cavalli , Stefano Crema , Jean Golay , Paulo Pereira , Aldina Piedade , Giordano Teza 
 / Attendance Mon, 09 Apr, 17:30–19:00  / Hall X3
The interactions between geo-environmental and anthropic processes and factors are increasing due to the increasing population and energy consumption (and the related side effects, e.g., urban sprawl, land degradation, natural resources 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 crucial for the analysis and modelling of the processes of interest in Earth and Soil Sciences. Spatial geo-environmental data require advanced mathematical, statistical and geomorphometric methodologies in order to fully exploit their informative content.
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 presenting intuitive and applied mathematical/numerical approaches and highlighting their key potentialities and limitations are particularly solicited. 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 (not limited to!):
1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty: geostatistics, machine learning, statistical learning theory, etc.
2) Analysis and representation of exhaustive spatial data at different scales and resolutions: geomorphometry, image analysis, pattern recognition, etc.