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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. Collaborative Recommender System for Spatio-Temporal Intelligence Documents

    SBC: NUMERICA CORP            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
  2. Multi-hop processing for OTHR range extension

    SBC: Decibel Research, Inc.            Topic: NGA191011

    The development of sophisticated anti-access/area denial (A2/AD) capabilities by our adversaries requires us to develop long range capabilities to mitigate this A2/AD threat. Extending the range of Over The Horizon Radars beyond their conventional single hop operating mode will potentially provide coverage out to 10000 km and beyond. We propose combining existing state-of-the art HF radar ray prop ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  3. VSIIR: VNIIRS Semantics Inference for Interpretability and Rating

    SBC: Intelligent Automation, Inc.            Topic: NGA191003

    Video analysts have to sift through voluminous video data to extract information of interest. The NGA uses the VNIIRS scale to rate videos with subjective interpretability. VNIIRS rating provides a meaningful way of organizing video browsing and search. Manually annotating videos with VNIIRS rating however, is very tedious. We propose to develop an automated tool which uses several cues such as mo ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  4. 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
  5. VAST-CQA Video Annotation and Statistics Toolkit for Crowdsoucing Quality Assessment

    SBC: Intelligent Automation, Inc.            Topic: NGA191002

    Intelligent Automation Inc. (IAI), along with our collaborators propose to develop a video tagging and statistics toolkit called VAST-CQA (Video Annotation and Statistics Toolkit for Crowdsourcing Quality Assessment). The key idea of the proposed approach is to provide a video quality annotation toolkit which reduces the effects of bias, subjective assessment and other human factors using a set of ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  6. Video Tagging and Interpretability Rating (VTIR) Toolkit Assisting VNIIRS Ground Truth Experiment

    SBC: INTELLIGENT FUSION TECHNOLOGY, INC.            Topic: NGA191002

    The Video National Imagery Interpretability Rating Scale (VNIIRS) defines different levels of interpretability based on the types of tasks an analyst can perform with videos of a given VNIIRS rating. DoD users of motion imagery rely on NGA to rate the interpretability of motion image clips and understand the factors affecting the VNIIRS of operational imagery. To develop and validate and verify an ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  7. Asynchronous Multi-transmitter Multi-aperture Synthetic 3D Imaging System

    SBC: VOXTEL, INC.            Topic: NGA183001

    Traditional active 3D imaging systems, such as airborne and terrestrial lidar scanners, use a transmitter and receiver typically co-located on the same platform and connected in synchronous communications. However, recent advances in laser, detector, and airborne systems technology have opened the door to smaller, higher-performance and significantly lower-cost airborne lidar systems in which it i ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  8. 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
  9. DNC-GD: Deep Neural Network Compression for Geospatial Data

    SBC: Intelligent Automation, Inc.            Topic: NGA181009

    Following technology advances in high-performance computation systems and fast growth of data acquisition, a technical breakthroughnamed Deep Learning made remarkable success in many research areas and applications. Nevertheless, the progress of hardwaredevelopment still falls far behind the upscaling of deep neural network (DNN) models at the software level. NGA seeks to apply neuralnetwork minia ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. TOFENetTopographic Features Extraction Network

    SBC: Intelligent Automation, Inc.            Topic: NGA181001

    Topographic features found in ground-based natural images contain information that is useful for a variety of applications including locationestimation and navigation. Traditionally these features have been manually labeled by analysts which is costly and time consuming, especiallyconsidering the volume of readily available data. We propose a novel method for extracting topographic features from s ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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