DoMars16k: A Diverse Dataset for Weakly Supervised Geomorphologic Analysis on Mars

The dataset contains 16150 samples extracted from 163 CTX images. Each sample depicts one of fifteen Martian surface landforms. One CTX image contributed with at least three and at most 1247 patches to the creation of the dataset. The dataset is subdivided into training, test, and validation sets, which contain seventy, ten, and twenty percent of the samples. The sets are mutually exclusive. Each sample has a size of 200 x 200 px or roughly 1.2km x 1.2km.

Contents

• data.zip contains the dataset separated into training, validation, and test sets. 
• models.zip contains pre-trained neural networks.

Classes

• Aeolian Bedforms
  • Aeolian Curved (ael)
  • Aeolian Straight (aec)
• Topographic Landforms
  • Cliff (cli)
  • Ridge (rid)
  • Channel (fsf)
  • Mounds (sfe)
• Slope Feature Landforms
  • Gullies (fsg)
  • Slope Streaks (fse)
  • Mass Wasting (fss)
• Impact Landforms
  • Crater (cra)
  • Crater Field (sfx)
• Basic Terrain Landforms
  • Mixed Terrain (mix)
  • Rough Terrain (rou)
  • Smooth Terrain (smo)
  • Textured Terrain (tex)

Code

Python code to train, evaluate, and apply deep neural networks to Martian surface data is available at GitHub: https://github.com/thowilh/geomars

Attribution

If you find this work useful please consider citing:

Wilhelm, T.; Geis, M.; Püttschneider, J.; Sievernich, T.; Weber, T.; Wohlfarth, K.; Wöhler, C. DoMars16k: A Diverse Dataset for Weakly Supervised Geomorphologic Analysis on Mars. Remote Sens. 2020, 12, 3981.

@article{wilhelm2020domars16k,
  doi = {10.3390/rs12233981},
  url = {https://doi.org/10.3390/rs12233981},
  year = {2020},
  month = dec,
  publisher = {{MDPI} {AG}},
  volume = {12},
  number = {23},
  pages = {3981},
  author = {Thorsten Wilhelm and Melina Geis and Jens P\"{u}ttschneider and Timo Sievernich and Tobias Weber and Kay Wohlfarth and Christian W\"{o}hler},
  title = {{DoMars}16k: A Diverse Dataset for Weakly Supervised Geomorphologic Analysis on Mars},
  journal = {Remote Sensing}
}