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Light Detection and Ranging (LIDAR) Surface Feature Extraction Tool

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N61339-06-C-0055
Agency Tracking Number: N043-245-0533
Amount: $1,000,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N04-245
Solicitation Number: 2004.3
Solicitation Year: 2004
Award Year: 2006
Award Start Date (Proposal Award Date): 2006-03-20
Award End Date (Contract End Date): 2008-03-09
Small Business Information
P.O. Box 8226
Missoula, MT 59807
United States
DUNS: 150373442
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Stuart Blundell
 (406) 829-1384
Business Contact
 David Opitz
Title: CEO
Phone: (406) 829-1384
Research Institution

NAVAIR requires accurate and timely 3-D geospecific features, terrain, and imagery data to support Modeling and Simulation (M&S) software applications used in training and pre-mission rehearsal simulators. LIDAR data provides an excellent source for spatially accurate digital terrain and 3-D feature information; however, current visual database production techniques require hundreds of man-hours to extract and attribute features. Automated processes are necessary to achieve the Navy’s goal of good M&S data that is efficiently processed and correctly formatted. Bottlenecks in producing good data are a result of manual processes used in (1) registering old features and imagery to newly acquired imagery and LIDAR, (2) extracting and editing complex 3-D geometries, and (3) updating and attributing features. The proposed Phase II workplan promises to significantly alleviate these bottlenecks by developing a system that exploits the information content of LIDAR and multiband imagery using machine learning-based feature extraction technology. The envisioned system will have the capability of automating the extraction of M&S features from LIDAR, registering features to images, allowing dynamic interaction with analysts, interrogating analysts when it needs additional information, and embedding the ability to create a continuously updated repository of feature extraction models.

* Information listed above is at the time of submission. *

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