Abstraction and Model Simplification to Identify Interesting Data (RAMS)

Remote sensing platforms are often able to transfer only a small portion of all collected data to end-users, requiring significant manual effort to select the most relevant information for analysis. To address this challenge, the ATC-NY team will develop Response Abstraction and Model Simplification (RAMS), a decision-support tool that assists scientists and automates remote and deep-space data collection for known events. RAMS operates efficiently on remote sensing platforms by quantizing samples of telemetry data to enable highly parallel processing of Quantized Neural Network (QNN) operations. RAMS also applies transfer learning and active learning techniques to train effective event detection models that reproduce human data-selection processes using a limited number of examples. Using RAMS, scientists supporting the Magnetospheric Multiscale (MMS) mission identify several examples of target signals for magnetic reconnection events near the Earth’s magnetopause and magnetotail, which RAMS uses to automatically select such events in future data.

RAMS can be applied to improve data collection, as well as automate and enhance event detection in NASA missions involving remote sensing with limited data access. Applications of RAMS include Earth-observing, atmospheric, and magnetospheric survey missions, such as MMS, WIND, THEMIS, Cluster II, STEREO, and the Europa Lander.

RAMS has application in commercial and government Geographic Information Systems (GIS). Long-running surveillance operations, including law enforcement, energy and utility monitoring, as well as security systems, can employ RAMS to reduce manual effort and quickly identify time-critical events at the point of occurrence to improve incident response time.

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