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Table of Contents
General introduction to our ML activity
Objectives
The main objectives of the machine learning work package are:
- To develop machine learning (ML) tools, designed for and tested on planetary science cases submitted by the community, and to provide sustainable, open access to the resulting products, together with support documentation
- To foster wider use of ML technologies in data driven space research, demonstrate ML capabilities and generate a wider discussion on further possible applications of ML
The goal is to build a multipurpose toolset for ML-based data analysis that will be applicable to a range of scientific research questions in planetary science with minor or easily-achievable customization efforts.
The tools will be shared and made accessible to the wider planetary science community through this webpage, the ML Portal. The ML work package will also provide documentation, tutorials, and workhops, to support the beneficiaries and the community users of the ML tools.
Methodology
In order to reach our goal of building versatile ML based tools for data analysis in planetary science, we pursue a bottom up approach, i.e., building tools that solve a diverse set of specific problems in planetary science with the aim of generalizing the solutions as much as possible. To achieve this, the community was asked to provide scientific use cases during the proposal phase of the project. Out of these proposed cases, a representative set of science cases was selected and the respective institutes were included as beneficiaries.
The idea is that the technical partners (KNOW, Uni-Passau and ACRI-ST) will closely collaborate with the scientific partners of our WP to build tools that solve “real life” scientific problems and at the same time provide the flexibility necessary to adapt the resulting toolset to other related problems in planetary science with passable effort.
A careful assessment of these science cases provided a solid basis to determine the technologies that would be best fitted to build the tools. It turned out that ML tools to classify, filter and search time series data as well as Deep Learning (DL) based classifiers of 2D (e.g., surface images or spectra) and video data, such as Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) in general as well as other techniques (as e.g., Self Organizing Maps) will be applied. Supervised as well as unsupervised approaches, with the latter also being able to leverage unlabelled data, are the technologies expected to benefit the community the most.
Furthermore, we will investigate automated pre-processing methods based on automated machine learning in order to optimize the pre-processing step per science case. The respective tools will be built in an iterative process, continuously assessed and validated by the scientific partners as well as the planetary science community in general. Hands on sessions at conferences and workshops will support the process of gathering feedback to validate and further optimize the developed solutions.
Work Package Beneficiaries and Partners
Work Package Beneficiaries and Partners |
|
ACRI-ST | ACRI-ST, France |
AOP | Armagh Observatory and Planetarium, Ireland |
DLR | Deutsches Zentrum für Luft- und Raumfahrt, Germany |
KNOW | Know-Center GmbH, Austria |
IAP-CAS | Institute of Atmospheric Physics, Academy of Sciences of Czech Republic, Czech Republic |
INAF | National Institute for Astrophysics, Italy |
IWF-OEAW | Space Research Institute, Austrian Academy of Sciences, Austria |
LMSU | M.V. Lomonosov Moscow State University, Russia |
UNIPASSAU | University of Passau, Germany |
Science Cases
During the proposal phase of Europlanet 2024 RI, the scientific community was asked to submit so-called science cases - problems where machine learning approaches seem promising. The following science cases where selected and are now worked on in our work package.