The primary objective of this proposal is to develop software technologies that leverage machine learning to enhance JPL's mission planning and data discovery capabilities. This SURP effort will: (1) Develop software to highlight novel or anomalous spectral and morphological features within the large amounts of high-dimensional data returned by planetary missions. Directing attention to these key features can help accelerate mission science planning and minimize missed science opportunities. We will primarily focus on data returned by visible and multispectral rover-based imagers such as Pancam, Mastcam, and Mastcam-Z. (2) Adapt these algorithms for other types of missions and instruments, including orbiting multispectral imagers such as PMI on Psyche or MODIS on Terra/Aqua, hyperspectral imagers such as CRISM or AVIRIS-NG, and other rover-based instruments such as DAN on MSL. (3) Work with the Planetary Data System (PDS) to incorporate intelligent data processing models to improve access, organization, and searching of data.
|Effective start/end date||10/17/18 → 9/29/19|
- National Aeronautics Space Administration (NASA): $50,000.00
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