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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. 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
  2. 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
  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. 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
  5. 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
  6. TopoRobo (Topographical Annotation Robot Software)

    SBC: Next Century Corporation            Topic: NGA181001

    Next Century Corporation proposes to create TopoRobo (topographical annotation robot software) to automatically extract depth-orderedlists of ridge polylines overlaid on an image or video mosaics to feed topography-based geolocation algorithms. TopoRobo will leverage deepneural network machine learning methods optimized for topographical features through EvoDevo, Next Centurys algorithm to grow an ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  7. 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
  8. CloudVS: Next Generation Video Services In Cloud Computing

    SBC: INTELLIGENT AUTOMATION, INC.            Topic: NGA181002

    With the explosive growth of Internet of Things (IoT) and mobile communication technologies, media streaming service and applications basedon video content have gained remarkable popularity and interest from users. When someone is using their device to record a video, then sharethat video with friends through a certain website (e.g. Netflix, YouTube), the process may sound simple from the user sid ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  9. Low-Shot Detection in Remote Sensing Imagery

    SBC: Next Century Corporation            Topic: NGA172002

    Next Century Corporation proposes the development of Muggsy, a low-shot deep learning detection prototype system that learns to recognize uncommon targets in remote imagery. Our Phase I research extends and leverages an image classification system of our own design called EvoDevo. EvoDevo evolves its own neural network architecture before training to meet the complexity of the data. Muggsy uses le ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. GFNet: Gnostic Fields based Low-Shot Learning for Target Detection in Remote Sensing

    SBC: INTELLIGENT AUTOMATION, INC.            Topic: NGA172002

    To detect uncommon targets in remote sensing imagery, it is quite often that very few prior examples are available. This so-called low-shot detection remains a very challenging problem in remote sensing, despite the recent development in state-of-the-art object detection algorithms such as Faster R-CNN and YOLO, and low-shot learning methods such as feature shrinking, model regression and memory a ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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