Seer Tracking

Artificial Intelligence Solutions to Track and Map Space Debris


Our mission is to create a safer Low Earth Orbit (LEO) environment for space missions. By providing accurate debris tracking and mapping capabilities, we hope to enable the next generation of space enterprises to drive a new wave of innovation in space technology, communication, data science, and remote sensing. 

Seer Tracking is a privately held company based in Palo Alto, CA. Our vision is to provide space debris classification and tracking services to commercial constellation satellite companies, private spaceflight operations, and government space agencies. Seer Tracking resolves complex space debris tracking problems by utilizing Artificial Neural Networks (ANNs) capable of recognizing orbital patterns and grouping clouds of debris. We ultimately seek to provide an accurate alternative to the current space debris tracking technology.


Our software uses Iterative Closest Point (ICP) technology in conjunction with Artificial Intelligence to provide accurate space debris tracking.

Debris Classification

Categorize, classify, and monitor  space debris using 3D point cloud data

Point Cloud Registration

Group debris orbiting in close vicinity as an entity for debris cloud tracking 

Machine Learning

Predict position and velocity of debris from patterns using neural networks trained with pattern recognition schemes.


Space Debris Cloud Pattern Recognition

Hundreds of space debris are registered into the cloud by the ICP 3D data alignment.  Trajectories of space debris clouds with the ICP 3D body transformation are tracked and then predicted by the machine learning neural networks trained beforehand.  

Orbital Tracking Results

The tracking errors of the position and velocity of each space debris over the true anomaly are demonstrated in terms of x-y-z axes in six plots. Thirty predicted trajectories and the truth are illustrated to overlay one another in three axes, respectively. Animation of this debris cloud tracking is implemented and shown in the center.

Our unparalleled use of Artificial Neural Networks (ANNs) for orbital pattern recognition, debris classification, and debris cloud formation creates powerful orbital prediction models that enable robust planning and implementation of space missions. 

Visualize real time debris mapping and orbit determination refinement on our novel visualization tool. 




Amber is earning her degrees in Physics and Computer Science from Stanford University. In 2017, Amber won the Intel Foundation Young Scientist Award, a top award at the Intel International Science and Engineering Fair. Amber has presented her research at the European Organization for Nuclear Research (CERN) in Geneva, Switzerland and at the TEDx Conferences. Most recently, she has been named to the Forbes 30 Under 30 list.

Amber Yang

Founder and CEO




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