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T0335-B

Satellite for Natural and Artificial Plumes (SNAP)

PI: Ben Gorr, Daniel Selva (Co-I), Texas A&M SEAK Lab

The Satellite for Natural and Artificial Plumes (SNAP) project aims to detect and classify natural and anthropogenic plumes in real time, which could enable timely tracking of short-timescale events (current solutions rely on post-processing of downlinked data). Specific plume cases can be generalized to any event detectable by computer-vision techniques. SNAP will use gimbaled cameras to identify and track plumes. The data from the cameras (visible to mid-infrared [IR]) will be processed by a trained neural network to quickly identify plumes. Plume properties, location, and images will be determined and downlinked.

Technology Areas (?)
  • NA
Problem Statement

Current plume detection and classification solutions rely on post-processing of downlinked data and do not actively track events of interest. Real-time detection could enable real-time tracking of short time-scale events and reduce the amount of data that needs to be downlinked. Specific plume cases can be generalized to any event detectable by computer vision techniques.

Technology Maturation

Flight tests are expected to provide real-time plume identification, accurate plume geolocation (with accompanying images/video), and real-time event tracking using neural network image processing within a small satellite (i.e., a 3-unit CubeSat with an instrument weighing approximately 6 kg). The flight tests aim to advance this innovation’s technology readiness level (TRL) from TRL 4 to TRL 6.

Future Customers

Real-time plume identification, geolocation, and event tracking
Fire, pollution, and volcanic activity monitoring for terrestrial and space-based applications
NASA, National Oceanic and Atmospheric Administration, U.S. EPA, U.S. Forest Service missions and research

Technology Details

  • Selection Date
    TechLeap21 (Sep 2021)
  • Program Status
    Active
  • Current TRL (?)
    Unknown
    Successful FOP Flights
  • 0 Balloon

Development Team

  • PI
    Ben Gorr
  • PI Organization
    Texas A&M SEAK Lab
  • Co-I
    Daniel Selva
  • Co-I Organization
    Texas A&M SEAK Lab
  • Sponsor

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