Award Data

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The Award database is continually updated throughout the year. As a result, data for FY19 is not expected to be complete until June, 2020.

  1. Variational Object Recognition and Grouping Network

    SBC: Intellisense Systems, Inc.            Topic: NGA181005

    To address the National Geospatial-Intelligence Agency (NGA) need for overhead imagery analysis algorithms that provide uncertaintymeasures for object recognition and aggregation, Intellisense Systems, Inc. (ISS) proposes to develop a new Variational Object Recognition andGrouping Network (VORGNet) system. It is based on the innovation of implementing a Bayesian convolutional neural network (CNN) ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  2. Bayesian Urban Degradation Assessment

    SBC: Intellisense Systems, Inc.            Topic: NGA181004

    To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  3. Blending Ground View and Overhead Models

    SBC: Arete Associates            Topic: NGA181008

    We propose to build ARGON, the ARet Ground-to-Overhead Network. The network will ingest analyst-supplied ground-level imagery ofobjects and retrieve instances of those objects in overhead collections, providing tips back to the analysts. A proprietary method of trainingthe network, leveraging in-house capabilities, data sources, and tools, will be critical to its success. During Phase I, we will p ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  4. Improving Uncertainty Estimation with Neural Graphical Models

    SBC: MAYACHITRA, INC.            Topic: NGA181005

    Building interpretable, composable autonomous systems requires consideration of uncertainties in the decisions and detections theygenerate. Human analysts need accurate absolute measures of probability to determine how to interpret and use the sometimes noisy resultsof machine learning systems; and composable autonomous systems need to be able to propagate uncertainties so that later reasoningsyst ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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