Machine Learning for Planetary In-Situ Spectroscopic Data

Rammelkamp, Kristin and von Canal, Alexander (2021) Machine Learning for Planetary In-Situ Spectroscopic Data. 2nd Workshop on Sensor AI, 2021-11-24, online. (Unpublished)

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Abstract

Summary of planned activities of the newly founded DLR junior research group "Machine learning for planetary in-situ spectroscopic data"

Item URL in elib:

https://elib.dlr.de/146968/

Document Type:

Conference or Workshop Item (Speech)

Title:

Machine Learning for Planetary In-Situ Spectroscopic Data

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Rammelkamp, Kristin	Kristin.Rammelkamp (at) dlr.de	https://orcid.org/0000-0003-4808-0823	UNSPECIFIED
von Canal, Alexander	alexander.voncanal (at) dlr.de	UNSPECIFIED	UNSPECIFIED

Date:

November 2021

Refereed publication:

Yes

Open Access:

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Unpublished

Keywords:

machine learning, laser-induced breakdown spectroscopy, Raman spectroscopy, Mars, planetary exploration

Event Title:

2nd Workshop on Sensor AI

Event Location:

online

Event Type:

international Conference

Event Date:

24 November 2021

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Space

HGF - Program Themes:

Robotics

DLR - Research area:

Raumfahrt

DLR - Program:

R RO - Robotics

DLR - Research theme (Project):

R - Planetary Exploration, R - Artificial Intelligence

Location:

Berlin-Adlershof

Institutes and Institutions:

Institute of Optical Sensor Systems

Deposited By:

Rammelkamp, Kristin

Deposited On:

09 Dec 2021 09:44

Last Modified:

24 Apr 2024 20:45

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