Near Earth Objects Machine Learning

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52 solutions from 45 locations were created.

For this challenge, we invite you to become "virtual contributors" to the Asteroid Grand Challenge and develop a hypothetical method, concept note or simple prototype that demonstrates how Machine Learning could be used to help us avoid the same fate as the dinosaurs.


There are millions of yet undiscovered Near Earth Objects (NEOs) which could pose a threat to Planet Earth. These Asteroids require space-based hardware to locate and track, however once their position is identified, follow-up observations can be made with radar or optical telescopes gathering light curve data - enabling estimates of composition, reflectivity, rotation and other characteristics that inform mitigation strategies to deflect objects before they impact with Earth. Presently, only a handful of hazardous NEOs have been detected prior to entering our atmosphere. The immense task of asteroid hunting is further complicated by the high number of false positives and long duration between observations - where some NEOs have orbits of many decades. Presented with these challenges, the space community has begun to look towards "machine learning" to both mechanize and accelerate the speed of detection and characterization.


Sample Areas to Explore:

  • Machine Learning tool could be used to remove known false positives
  • Align astrometry of newly observed objects and already tagged objects from archived surveys
  • Machine vision and drones could radically increase the number of found meteorite falls
  • Theoretical approaches, simulations and demos will be accepted

Tip: The Minor Planet Center currently acts as the central clearinghouse for asteroid observations taken as data from professional and amateur telescopes, and space-based observatories such as NASA’s NEOWISE. This astrometric data allows the calculation of orbits for the asteroids so both professionals and amateurs may conduct follow-up observations from the ground.

Project teams from 45 locations solved Near Earth Objects Machine Learning
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Deborah Thermidor 3 years ago
u know this is online right
Deborah Thermidor 3 years ago
as a class i loved to spend my year with u peeps
Francis Yee 2 years ago
Network your satellites.
Let all satellites talk to each other to make a orbital sensor grid.
Used the CCSDS satellite communications protocol.
By triangulating and using machine learning in near earth orbit satellites you already have in place a sensor grid to detect incoming space objects.
Most likely astroid come from
Make a orbiting space sensor grid.
Use machine learning to monitor Near Earth Objects in space.
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