Defense Advanced Research Projects AgencyTagged Content List

Imagery and Visualization

Visual representations of data and information

Showing 16 results for Imagery + Programs RSS
The United States Government has an interest in developing and maintaining a strategic understanding of events, situations, and trends around the world, in a variety of domains. The information used in developing this understanding comes from many disparate sources, in a variety of genres, and data types, and as a mixture of structured and unstructured data. Unstructured data can include text or speech in English and a variety of other languages, as well as images, videos, and other sensor information.
The ability to see farther, with higher clarity, and through darkness and/or obscurants, is vital to nearly all military operations. At the same time, for advanced imaging systems there is an immense need to increase field of view (FOV), resolution, and day/night capability at reduced size, weight and power (SWaP) and cost. The main driver for these requirements is the need to provide dismounted soldiers and near-ground support platforms with the best available imaging tools to enhance combat effectiveness.
Current infrared systems either have a narrow field of view, slow frame rates or are low resolution. DARPA's Autonomous Real-Time Ground Ubiquitous Surveillance - Infrared (ARGUS-IR) program will break this paradigm by producing a wide-field-of-view IR imaging system with frame rates and resolution that are compatible with the tracking of dismounted personnel at night. ARGUS-IR will provide at least 130 independently steerable video streams to enable real-time tracking of individual targets throughout the field of view. The ARGUS-IR system will also provide continuous updates of the entire field of view for enhanced situational awareness.
The goal of the EXTREME Program is to develop new optical components, devices, systems, architectures and design tools using Engineered Optical Materials (EnMats) to enable new functionality and/or vastly improve size, weight, and power characteristics of traditional optical systems. EnMats are broadly defined to include, but are not limited to, metamaterials (both metallic and dielectric), scattering surfaces and volumes, holographic structures, and diffractive elements.
The Geospatial Cloud Analytics (GCA) program is developing technology to rapidly access the most up-to-date commercial and open-source satellite imagery, as well as automated machine learning tools to analyze this data. Current approaches to geospatial analysis are ad hoc and time intensive, as they require gathering and curating data from a large number of available sources, downloading the data to specific locations, and running it through separate suites of analytics tools.