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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

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. Innovative methods for detecting and characterizing electrical grid topologies and induced electrical power transient events from lights

    SBC: Systems & Technology Research LLC            Topic: NGA191010

    STR is proposing to implement monitoring of power grid state via high-speed, wide-field optical photometry. We will design, test, and implement algorithms on commercially available hardware with the intent of deriving grid topology in addition to detecting, characterizing, and geolocating anomalous events. We will also evaluate fusing the photometry-derived data with other data sources available t ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  2. Automating Procedural Modeling of Buildings from Point Cloud Data

    SBC: Dignitas Technologies, LLC            Topic: NGA183002

    Newer techniques in data collection such as Lidar and photogrammetry can provide large quantities of accurate and up-to-date source data models in operational areas, but transforming this often massive amount of raw source data into a lightweight 3D representation that can be quickly consumed by defense customers using a web browser or mobile devices remains a challenging problem. While point clou ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  3. Generalized Change Detection to Cue Regions of Interest

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181006

    Toyon proposes to research and develop algorithms for generalized salient change detection, and to incorporate these algorithms into software tools implemented on the cloud. Our approach leverages the two most promising methods from Phase I, both based on supervised learning. The first method is the entropy-based feature vector and corresponding neural network, which we will apply at a coarse sear ...

    SBIR Phase II 2019 Department of DefenseNational Geospatial-Intelligence Agency
  4. Video to Feature Data Association and

    SBC: Novateur Research Solutions, LLC            Topic: NGA181007

    This SBIR Phase II project proposes a probabilistic approach to determine a vehicle’s location using onboard video and Lidar sensors and foundation map data in GPS denied environments. The proposed system does not rely on only one type of information source, instead it combines proposals from a variety of location estimators to find a vehicles location in GPS-denied environments. The system take ...

    SBIR Phase II 2019 Department of DefenseNational Geospatial-Intelligence Agency
  5. Gamified Analysis Tasks for Heightened Engagement across Repetitions (GATHER)

    SBC: Charles River Analytics, Inc.            Topic: NGA191007

    At the National Geospatial-Intelligence Agency (NGA), the ability to serve and analyze data is crucial to the success of efforts ranging from disaster relief to strategic military support. NGA recently created the Office of Automation, Augmentation, and Artificial Intelligence (AAA) which has a goal “to automate routine tasks to give crucial time back to employees.” These automated systems mus ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  6. A User-Centric Gamified Crowdsourcing System for Geospatial Analysts

    SBC: 361 Interactive, Llc            Topic: NGA191007

    Creating comprehensive geospatial datasets requires that National Geospatial Agency (NGA) analysts spend large amounts of time searching for, delineating, and labeling non-moving features in overhead imagery. This tiresome and tedious task can negatively impact not only the analysts’ work satisfaction but also the resulting data quality. Fortunately, recent advances in gamification and crowdsour ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  7. 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
  8. Collaborative Recommender System for Spatio-Temporal Intelligence Documents

    SBC: RAJI BASKARAN LLC            Topic: NGA191005

    NLP pipelines available today are getting robust for general language modeling purposes. But domain-specific data, abbreviations and lingos, and text about time or space still need a lot of tuning and training that are well beyond application of standard tool sets. Deep learning for recommendation engines is quite new, and all recommender systems, in particular for specially trained users, tend to ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  9. ALARM: Adversarially-learned Labels using Activity and Reward Models

    SBC: Aptima, Inc.            Topic: NGA191006

    Technological advances in navigation and positioning, along with expanding wireless infrastructure and remote sensing technologies, have resulted in an explosive growth of available trajectory data from a variety of moving objects, such as people, cars, ships, or animals. Traditional trajectory mining algorithms do not explain how and why the motion was generated, limiting their utility in GEOINT ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  10. Automating the Semantic Labeling of Trajectory Data

    SBC: Intelligent Models Plus Inc.            Topic: NGA191006

    Advances in location-acquisition and mobile computing techniques have generated massive spatiotemporal trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Moreover, recent research has tabbed learning of how to automatically explain and anticipate both the observable and abstract trajectories as one of the likely keys to building t ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
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