English

領域外・複数領域 (M)
セッション小記号 計測技術・研究手法 (TT)
セッション ID M-TT43
タイトル Machine Learning in Planetary Sciences
タイトル短縮名 Machine Learning in Planetary Sciences
開催日時 口頭セッション 5/22(日) PM2
現地会場
現地ポスターコアタイム 5/22(日) PM3
代表コンビーナ 氏名 Mario D'Amore
所属 German Aerospace Center DLR Berlin
共同コンビーナ 1 氏名 Ute Amerstorfer
所属 Austrian Academy of Sciences
セッション言語 E
』スコープ 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.
発表方法 口頭およびポスターセッション
共催情報 学協会 -
ジョイント -