SBIR/STTR Research Award

Open-Source Pipeline for Large-Scale Data Processing, Analysis and Collaboration

Award identifier e6037e68ca0e20c3860ffb25fb6835779d2770c612f5154f3bb25f4ffbe21fb2Awarding agency National Aeronautics and Space Administration

Award amount

$124,977

Program and phase

SBIR · Phase I

Award year

2017

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Award details

Awarding agency
National Aeronautics and Space Administration
Branch
Not reported
Recipient company
CREARE LLC
Proposal award date
2017-06-09
Contract end date
2017-12-08
UEI
JDKYAKDT4TT6
Solicitation number
Not reported
Topic code
S5.03

Research abstract

NASA's observational and modeled data products encompass petabytes of earth science data available for analysis, analytics, and exploitation. Unfortunately, these data are underutilized due to vast computational resource requirements; disparate formats, projections, and resolutions that hinder data fusion and integrated analyses; complex and disjoint data access and retrieval protocols; and task-specific and non-reusable code development processes that hinder algorithm sharing and collaboration. Due to these limitations, recent advances in unsupervised machine learning using deep neural nets (DNNs) have not been widely adopted for applications such as pixel-based classification, image preprocessing, feature recognition, and scene understanding. Creare proposes to develop an open-source, standards-based Python software framework that removes major barriers to widespread exploitation of geospatial earth science data. This will be achieved through development of PODPAC (Pipeline for Observational Data Processing, Analysis, and Collaboration), a pipeline-based architecture that (1) enables multi-scale and multi-windowed access, exploration, and integration of available earth science data sets to support both analysis and analytics; (2) automatically accounts for differences in underlying geospatial data formats, projections, and resolutions; (3) simplifies implementation and parallelization of geospatial data processing routines; (4) seamlessly integrates with DNN machine learning frameworks; and (5) unifies access, processing, and sharing of data and algorithms via interfaces to existing NASA repositories. To demonstrate the impact of these innovations, we will use PODPAC to derive an on-demand, high-resolution global soil moisture data product from the Soil Moisture Active/Passive (SMAP) satellite radiometer observational data to support applications in hydrology, agriculture, and humanitarian response missions involving flooding, drought, and water resources.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

1,365 SBIR/STTR records share this UEI (1983–2026). Reported award amounts total $507,020,394. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense, National Aeronautics and Space Administration, Department of Health and Human Services, Department of Energy, Department of Transportation, Department of Commerce

Agency intelligence

National Aeronautics and Space Administration

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