JpGU Meeting 2022
Session outline| Multidisciplinary and Interdisciplinary (M) | ||
|---|---|---|
| Session Sub-category | Technology & Techniques (TT) | |
| Session ID | M-TT43 | |
| Title | Machine Learning in Planetary Sciences | |
| Short Title | Machine Learning in Planetary Sciences | |
| Main Convener | Name | Mario D'Amore |
| Affiliation | German Aerospace Center DLR Berlin | |
| Co-Convener 1 | Name | Ute Amerstorfer |
| Affiliation | Austrian Academy of Sciences | |
| Session Language | E | |
| Scope | Due to an increasing amount of data from a continuously increasing number of spacecraft in our solar system, new frameworks for rapidly and intelligently extracting information from these data sets are needed. Machine learning provides such a framework for tackling a wide range of research questions in planetary sciences.
Machine learning approaches could improve existing models, creating computationally efficient algorithms for feature classification and regression problems, e.g. solar wind time series data, planetary surface images or hyperspectral data.
We encourage submissions dealing with machine learning approaches of all levels in planetary sciences. In this session, we aim to provide an overview of the current efforts to integrate machine learning technologies into data driven space research and to highlight state-of-the art developments. | |
| Presentation Format | Oral and Poster session | |
| Joint with | ||
| Co-sponsored | - | |