Dataset Open Access

Mars surface image (Curiosity rover) labeled data set

Alice Stanboli; Kiri Wagstaff

Other(s)
Joy Crisp

This data set consists of 6691 images that were collected by the Mars Science Laboratory (MSL, Curosity) rover by three instruments (Mastcam Right eye, Mastcam Left eye, and MAHLI).  These images are the "browse" version of each original data product, not full resolution.  They are roughly 256x256 pixels each.

We divided the MSL images into train, validation, and test data sets according to their sol (Martian day) of acquisition.  This strategy was chosen to model how the system will be used operationally with an image archive that grows over time.  The images were collected from sols 3 to 1060 (August 2012 to July 2015).  The exact train/validation/test splits are given in individual files.  Full-size images can be obtained from the PDS at https://pds-imaging.jpl.nasa.gov/search/ .

Contents:

  • calibrated/: Directory containing calibrated MSL images
  • train-calibrated-shuffled.txt: Training labels (images in shuffled order)
  • val-calibrated-shuffled.txt: Validation labels
  • test-calibrated-shuffled.txt: Test labels
  • msl_synset_words-indexed.txt: Mapping from class IDs to class names

Attribution:

If you use this data set in your own work, please cite this DOI:

10.5281/zenodo.1049137

Please also cite this paper, which provides additional details about the data set.

Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence, 2018.

Files (60.6 MB)
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msl-images.zip md5:b078d8d04d71e04ab7dcfad29ae0c07e 60.6 MB Download
  • Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence, 2018.

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