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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

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.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

  1. MONET: Modeling non-Objects and Novelty for Efficient Training

    SBC: KITWARE INC            Topic: OSD221003

    Object detection datasets for overhead imagery are typically generated using bootstrapping methods to reduce annotator effort and cost. These methods iteratively train a detector from a limited set of user-provided and model-predicted labels. Such approaches bias detectors toward the initial object set, limiting their capacity to handle object variations or discover novel objects classes. MONET ov ...

    SBIR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency
  2. Multi-Task Scale Aware Continuous and Localizable Embeddings

    SBC: KITWARE INC            Topic: OSD22A001

    NGA uses deep networks for many tasks including image registration, land cover segmentation, and object detection. Current deep learning approaches develop specialist networks for each task and type of data. Not only is this inefficient, because networks can’t be reused across tasks, this approach ignores correlations between tasks and data sources that can improve performance. In response, we w ...

    STTR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency
  3. Clarifai Proposal- Automating tilt and roll in ground-based photos and video frames

    SBC: CLARIFAI, INC.            Topic: NGA201006

    For this proposal, Clarifai would utilize internal expertise in computer vision and deep learning to pursue a CNN to camera orientation estimation. Specifically, we would adapt existing Clarifai models to extract image features and use these features as input to a camera orientation regressor. The horizon filter information might be combined with information from the encoder to produce a joint emb ...

    SBIR Phase I 2021 Department of DefenseNational Geospatial-Intelligence Agency
  4. Algorithm Performance Evaluation with Low Sample Size

    SBC: SIGNATURE RESEARCH, INC.            Topic: NGA20C001

    The team of Signature Research, Inc. and Michigan Technological University will develop and demonstrate methods and metrics to evaluate the performance of machine learning-based computer vision algorithms with low numbers of samples of labeled EO imagery. We will use the existing xView panchromatic dataset to demonstrate a proof-of-concept set of tools. If successful, in Phase II, we will extend t ...

    STTR Phase I 2021 Department of DefenseNational Geospatial-Intelligence Agency
  5. Clarifai proposal- Synthetic Data for Computer Vision

    SBC: CLARIFAI, INC.            Topic: NGA201001

    One of the most pressing problems at the forefront of machine learning and computer vision research is image recognition when high quality labeled data is difficult to acquire, typically due to the prohibitive cost of data acquisition and annotation. Clarifai has teamed with L3Harris to combine their unparalleled experience in simulating geospatial sensors with our award-winning computer vision ca ...

    SBIR Phase I 2021 Department of DefenseNational Geospatial-Intelligence Agency
  6. TrailBlazer: A GAN-Trained, High-Fidelity Track Simulator

    SBC: KITWARE INC            Topic: NGA192004

    Persistent wide area sensor coverage enables unique intelligence analytic capabilities such as pattern-of-life detection, unsupervised pattern discovery, and anomaly detection. As these capabilities incorporate machine learning and artificial intelligence techniques, large datasets are necessary for training and validation. However, the lack of datasets with high fidelity dynamic targets and acto ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  7. Sporadic long-Term and Transferable Patterns of life (SPOTTER)

    SBC: KITWARE INC            Topic: NGA192005

    Aerial or spaced-based imaging assets cannot continuously monitor a single location or site of interest for prolonged periods of time such as weeks, months, or years without significantly sacrificing surveillance of other locations. Current approaches for modeling patterns of life (PoL) at a location are not capable of incorporating sporadic data and do not gracefully model daily to monthly or ye ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  8. Danesfield Courier: Efficient Transmission and Rendering of CORE3D Models

    SBC: KITWARE INC            Topic: NGA183002

    An important application for geospatial 3D models is fast transmission and rendering for disadvantaged users who have only a web browser and a limited bandwidth connection. Point cloud models commonly used within the NGA are too large for efficient transmission and rendering. Kitware’s new research on the IARPA CORE3D program has demonstrated procedural building models from point clouds for more ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  9. 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
  10. 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
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