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Labelmars.det: Crowd-sourcing an extremely large high quality Martian image dataset.

Wallace, I.; Schwenzer, S. P.; Woods, M.; Read, N.; Wright, S.; Waumsley, K. and Joudrier, L. (2017). Labelmars.det: Crowd-sourcing an extremely large high quality Martian image dataset. In: 48th Lunar and Planetary Science Conference, 20-24 Mar 2017, Houston.

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The observation of landforms, outcrops and small features within a (Martian) landscape is key to the understanding of its geologic past as well as present environmental conditions. Studies of such features have – for example – revealed the nature of streambeds at Gale Crater, and allowed to study Aeolian bedforms as they were encountered by the Curiosity, and Spirit rovers. With two active rovers (Opportunity, Curiosity) currently on Mars, and two more to be launched in 2020 (ExoMars, Mars2020), the imaging data sets are a huge, growing resource, which need to be explored as best as possible.

LabelMars ( is a citizen science activity to collect geological annotations of Martian rover navigation camera images. As part of the ESA NOAH (Novelty Or Anomaly Hunter) project it will provide a large, high quality dataset to develop stateof-the-art machine vision algorithms for autonomous science detection, targeted at future rover missions.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 The Authors
Project Funding Details:
Funded Project NameProject IDFunding Body
Novelty or Anomaly Hunter300390Industry project
Keywords: high quality data set; crowd sourcing; Curiosity rover; Opportunity rover; Spirit rover
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Environment, Earth and Ecosystem Sciences
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Space
Related URLs:
Item ID: 48660
Depositing User: Susanne Schwenzer
Date Deposited: 27 Feb 2017 10:40
Last Modified: 02 May 2019 09:13
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