[Rockhounds] You can help a Mars Rover’s AI learn to tell rocks from dirt

[Kreigh Tomaszewski]

Mars Rover Curiosity has been on the Red Planet for going on eight years,
but its journey is nowhere near finished — and it’s still getting upgrades.
You can help it out by spending a few minutes labeling raw data
<https://www.zooniverse.org/projects/hiro-ono/ai4mars> to feed to its
terrain-scanning AI.

Curiosity doesn’t navigate on its own; there’s a whole team of people on
Earth
<https://techcrunch.com/2020/04/14/nasas-curiosity-team-is-operating-the-mars-rover-from-home/>
who
analyze the imagery coming back from Mars and plot a path forward for the
mobile science laboratory. In order to do so, however, they need to examine
the imagery carefully to understand exactly where rocks, soil, sand and
other features are.

This is exactly the type of task that machine learning systems are good at:
You give them a lot of images with the salient features on them labeled
clearly, and they learn to find similar features in unlabeled images.

The problem is that while there are lots of ready-made data sets of images
with faces, cats and cars labeled, there aren’t many of the Martian surface
annotated with different terrain types.

“Typically, hundreds of thousands of examples are needed to train a deep
learning algorithm. Algorithms for self-driving cars, for example, are
trained with numerous images of roads, signs, traffic lights, pedestrians
and other vehicles. Other public datasets for deep learning contain people,
animals and buildings — but no Martian landscapes,” said NASA/JPL
<https://crunchbase.com/organization/nasa> AI researcher Hiro Ono in a news
release
<https://mars.nasa.gov/news/8689/nasas-mars-rover-drivers-need-your-help/>.

So NASA is making one, and you can help.

https://techcrunch.com/2020/06/12/you-can-help-a-mars-rovers-ai-learn-to-tell-rocks-from-dirt/

https://www.zooniverse.org/projects/hiro-ono/ai4mars