ITS4.2/PS4.4 EDI

The increasing amount of data from an increasing number of spacecraft in our solar system shouts out for new data analysis strategies. There is a need for frameworks that can rapidly and intelligently extract information from these data sets in a manner useful for scientific analysis. The community is starting to respond to this need. Machine learning, with all of its different facets, provides a viable playground for tackling a wide range of research questions. Algorithms to automatically detect and classify special features in time series data of the solar wind or in 2D images of planetary surfaces are examples of where machine learning approaches can support and improve existing models. Further, modern learning methods can encode properties of interest in lower dimensional space, and thus making them more searchable.


We encourage submissions dealing with machine learning approaches of all levels in planetary sciences and heliophysics. The aim of this session is to provide an overview of the current efforts to integrate machine learning technologies into data driven space research, to highlight state-of-the art developments and to generate a wider discussion on further possible applications of machine learning.

Co-organized by ESSI1/ST1
Convener: Mario D'Amore | Co-conveners: Ute Amerstorfer, Sahib JulkaECSECS, Angelo Pio Rossi

The increasing amount of data from an increasing number of spacecraft in our solar system shouts out for new data analysis strategies. There is a need for frameworks that can rapidly and intelligently extract information from these data sets in a manner useful for scientific analysis. The community is starting to respond to this need. Machine learning, with all of its different facets, provides a viable playground for tackling a wide range of research questions. Algorithms to automatically detect and classify special features in time series data of the solar wind or in 2D images of planetary surfaces are examples of where machine learning approaches can support and improve existing models. Further, modern learning methods can encode properties of interest in lower dimensional space, and thus making them more searchable.


We encourage submissions dealing with machine learning approaches of all levels in planetary sciences and heliophysics. The aim of this session is to provide an overview of the current efforts to integrate machine learning technologies into data driven space research, to highlight state-of-the art developments and to generate a wider discussion on further possible applications of machine learning.