Using an online tool to label Martian terrain types, you can train an artificial intelligence algorithm that could improve the way engineers guide the Curiosity rover.
You may be able to help NASA's Curiosity rover drivers better navigate Mars. Using the online tool
AI4Mars to label terrain features in pictures downloaded from the Red Planet, you can train an artificial intelligence algorithm to automatically read the landscape.
Is that a big rock to the left? Could it be sand? Or maybe it's nice, flat bedrock. AI4Mars, which is hosted on the citizen science website Zooniverse, lets you draw boundaries around terrain and choose one of four labels. Those labels are key to sharpening the Martian terrain-classification algorithm called SPOC (Soil Property and Object Classification).
Developed at NASA's Jet Propulsion Laboratory, which has managed all of the agency's Mars rover missions, SPOC labels various terrain types, creating a visual map that helps mission team members determine which paths to take. SPOC is already in use, but the system could use further training.
"Typically, hundreds of thousands of examples are needed to train a deep learning algorithm," said Hiro Ono, an AI researcher at JPL. "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."
Once fully up to speed, SPOC will be able to automatically distinguish between cohesive soil, high rocks, flat bedrock and dangerous sand dunes, sending images to Earth that will make it easier to plan Curiosity's next moves.
"In the future, we hope this algorithm can become accurate enough to do other useful tasks, like predicting how likely a rover's wheels are to slip on different surfaces," Ono said.