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FDL 2016

FDL 1.0 was the initial six-week FDL conducted at NASA's Ames Research Center and the SETI Institute campus. Three teams studied meteorite hunting, deflector selector and asteroid shape modeling.

 
 
 
 
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Asteroid Shape Modeling

Production of 3D models of potentially hazardous asteroids for better assessment of their orbits and center of mass. 

VAEs & Bayesian optimization were used to simultaneously shape model and establish spin axis — achieving greater accuracy in a fraction of the time than human experts, enabling a rapid understanding of the shape of an asteroid while it is still being tracked by radar.

 
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Meteorite Hunting Drone

To help in the challenge of finding fresh meteorites, the FDL team designed a meteorite hunting drone to assist in the field. 

To train the drone, the team incorporated six deep learning models based on a library of 25,000 meteorite images, along with a 15 million image library of ‘everything else’, all driven by a user-friendly app for use by researchers around the world.

 
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Asteroid “Deflector Selector” Decision Support 

Assessment of the most effective deflection strategies for moving a potentially hazardous asteroid on collision with a town or city on Earth. 

The FDL team built a decision support tool that uses machine learning to analyze the effects of 1.5 million simulated deflection campaigns to determine which methods are the most effective.


The Deflector Selector enables strategic decisions on deflection technologies to prioritize, and what asteroid characteristics are the most important to know in advance of taking action.