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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

  1. Learning traffic camera locations using vehicle re-identification

    SBC: Arete Associates            Topic: NGA201005

    In its effort to provide necessary intelligence and analysis, the National Geospatial-Intelligence Agency (NGA) utilizes extensive traffic camera systems. However, the large amount of data overwhelms both analysts and existing processing methods. In order to provide a better understanding and reduce the search space for common problems such as target tracking, it is necessary to extract the camera ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  2. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  3. Collaborative Recommender System for Spatio-Temporal Intelligence Documents

    SBC: Numerica Corporation            Topic: NGA191005

    US military and intelligence agencies have invested significant resources in data collection and effective search and analytics tools. However, due to increasing amounts of data, finding relevant information has become more difficult. Thus, there is an important need for recommender system technology that pushes relevant un-queried data to analysts through automation and machine learning technique ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  4. A Multi-Branch Network for Automated VNIIRS Assessment of Motion Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA191003

    Due to the lack of consistency in existing automated methods for assigning VNIIRS levels to motion imagery, and the overwhelming human resources required to manually assign levels, a new method of automated/semi-automated VNIIRS assessment is needed. In recent years, advancements in deep learning have provided solutions to previously intractable computer vision problems. In many cases, automated d ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  5. Faster Optical Modem for Underwater Data Acquisition

    SBC: Sonalysts, Inc.            Topic: NGA182001

    To address NGA’s requirements, Sonalysts’ team of world-class experts in underwater optical communication proposes development and implementation of the Precision Optical Navigation Transceiver for Undersea Systems (PONTUS). PONTUS will transfer navigation information from an Underwater Navigation Beacon (UNB) to an Unmanned Undersea Vehicle (UUV) in an electromagnetic-spectrum-denied (e.g., G ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  6. Faster Optical Modem for Underwater Data Acquisition

    SBC: SA Photonics, Inc.            Topic: NGA182001

    SA Photonics’ Optical Navigation and Ranging (ONAR) system is an interrogative system that operate underwater in wavelength range of blue/green (450-540 nm) and enables navigational correction to IMU based dead reckoning navigation. The location based beacons are battery operated and have operational life span of over one year. The system is designed to operate in on demand burst mode so that no ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  7. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  8. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  9. 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
  10. 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
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