Award Data

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...