For more information, contact: email@example.com
It is common today for even consumer-grade cameras to tag the images and videos that they capture with the location of the image on the earth’s surface (geolocation). However, some imagery does not have a geolocation tag and it can be important to know the location of the camera, image, or objects in the scene. For this imagery, analysts work hard to deduce as much as they can using reference data from many sources, including overhead and ground-based images, digital elevation data, existing well-understood image collections, surface geology, geography, and cultural information. Such image/video geolocation is an extremely time-consuming and labor-intensive activity that often meets with limited success.
Several research and consumer-oriented systems have developed useful and relevant capabilities using techniques that include large-scale ground-level image acquisition, crowd-sourcing, and sophisticated image matching. These largely automated systems tend to work best in geographic areas with significant population densities or that are well traveled by tourists, and where the query image or video contains notable features such as mountains or buildings.
The Finder Program aims to build on existing research systems to develop technology that augments the analyst’s abilities to address the geolocation task. Required technical innovations include 1) the integration of analysts’ abilities and automated geolocation technologies to solve geolocation problems; 2) fusion of diverse, publicly-available, but often imperfect data sources; and 3) expansion of automated geolocation technologies to work efficiently and accurately over all terrain and large search areas. If successful, Finder will deliver rigorously tested solutions for the image/video geolocation task in any outdoor terrestrial location.
Performers (Prime Contractors)
Applied Research Associates, Inc.; BAE Systems Information & Electronic System Integration, Inc.; Leidos, Inc.; Object Video, Inc.;
- Geospatial fusion
- Data fusion
- Machine learning
- Big data
- Image processing
- Computer vision
- Natural language processing
To access Finder program-related publications, please visit Google Scholar.