I would be grateful if you would consider to submit an abstract to the session “Learning from spatial data: representation, inference and modelling in earth and soil sciences” on the next EGU (European Geosciences Union) meeting, to be held in Vienna (Austria), from 17–22 April 2016. The deadline for the receipt of abstracts is 13 Jan 2016, 13:00 CET (submission information at http://egu2016.eu//abstract_management/how_to_submit_an_abstract.html). It is deemed important to highlight that the EGU is committed to promoting the participation of both early career scientists and established researchers from low and middle income countries who wish to present their work at the EGU General Assembly (see http://www.egu.eu/ecs/financial-support ). Please, feel free to contact me for any information about the session. The details of the session are attached below or follow the link: http://meetingorganizer.copernicus.org/EGU2016/session/20486
Session: SSS12.11/GM2.4 Learning from spatial data: representation, inference and modelling in earth and soil sciences.
Convener: Sebastiano Trevisani ; Co-Conveners: Paulo Pereira , Jean Golay , Igor Bogunović , Marco Cavalli.
Abstract: Spatial and spatiotemporal data are crucial for the analysis and modelling of the processes of interest in Earth and Soil Sciences; the heterogeneity characterizing the typology and quality of available datasets coupled with the complexity of the studied phenomena require advanced mathematical and statistical methodologies in order to fully exploit the informative content at hand. The session aims to explore the challenges and potentialities of quantitative spatial data analysis and modelling in the context of Earth and Soil Sciences. Studies presenting applied mathematical approaches according to an intuitive approach and highlighting the key potentialities and limitations are particularly appreciated. The main interest is toward studies applying techniques and methodologies that make the data “talk” to us about the studied geo-environmental processes and factors; from this perspective we refers to a broad suite of mathematical and statistical techniques such as (but not limited to!):
• Machine learning • Statistical learning theory • Geostatistics • Geomorphometry and other GIS related techniques for terrain analysis • Pattern analysis and recognition • Expert systems (e.g., fuzzy systems) combining expert knowledge and spatial data • Alternative techniques of representation of spatial data (e.g.. visualization, sonification, haptic devices, etc.)
The session aims to discuss three key elements of spatial analysis, emphasizing the connections between spatial data and geo-environmental processes and factors: 1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty 2) Analysis and representation of exhaustive spatial data at different scales and resolutions (e.g., geomorphometry, pattern recognition, etc.) 3) Spatial modelling, possibly using the results from points 1 and 2, of the physicochemical processes and aspects of interest (e.g., surface flow processes, landslides susceptibility models, landscape evolution models, ecological modelling, etc.).
We think also to promote a special issue.