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SBC: INTELLISENSE SYSTEMS INC Topic: NGA181004
To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment ...SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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
SBC: INTERNATIONAL ASSOCIATION OF VIRTUAL ORGANIZATIONS, INCORPORATED Topic: NGA201006
NGA seeks an innovation to fully automate processes that recover camera orientation parameters, specifically for ground-based “photo” (aka image) and video frame use cases. The ability to use these ground-based systems represents an enhanced aspect to traditional photogrammetry, and in many regards, folding in hand-held systems, and considering the nuances associated with these collects, is ye ...SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
SBC: MAYACHITRA, INC. Topic: NGA181005
Building interpretable, composable autonomous systems requires consideration of uncertainties in the decisions and detections theygenerate. Human analysts need accurate absolute measures of probability to determine how to interpret and use the sometimes noisy resultsof machine learning systems; and composable autonomous systems need to be able to propagate uncertainties so that later reasoningsyst ...SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
SBC: GEOMETRIC DATA ANALYTICS INC. Topic: NGA20A001
We propose to develop a theoretical understanding of the relationship between intrinsic geometric structure in both training and latent data and characteristics of functions learned from that data for deep neural network (DNN) architectures. Along the way we propose to also understand the structure of the neural networks that are best trained on a given data set. Both of these theories will lead t ...STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
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
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