Simulation, Learning & Visualization (SimDeepVisu)

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Simulation, Learning & Visualization (SimDeepVisu)

Synopsis: Visualizing images in order to better understand data coming from measures,  simulation or machine/deep learning models is a part of the scientific reasoning process. How to improve the understanding of spatio-temporal phenomena on Earth and their related models, based on the visuo-spatial reasoning through data interaction? The objective of this thematic session is to make various researchers have a visual and interactive new perspective on their way to interpret their models and outputs, and to open new insights. 

New approaches in information visualization, data-driven storytelling, VR/AR, such as experimental or position papers from simulation or learning domains, addressing the need for visual interactive approaches, supporting comparison, filtering, reasoning, interpretation and explainability, are expected. 

The complexity of this issue comes from:

  • the complexity of the spatio-temporal phenomena and their related models to analyze and interpret, according to the targeted final users;
  • the amount of multi-sources multi-scales observations and simulated, predicted, annotated, learned, or raw data, and their imprecision;
  • the complexity to co-visualize multiple heterogeneous and imprecise data, in order to get a comprehensive point of view on a phenomenon, at any scale.

This thematic session aims at favoring a new dynamic of multidisciplinary research, between various methodological communities around geospatial applications. 

The session is open to all authors.