SBIR-STTR Award

The U.S. Army requires automated feature extraction (AFE) software for collecting very high-resolution 3D urban features from terrestrial LIDAR data to support the ground-based Warfighter operating in the urban battlespace. Advanced vehicle-mounted and man-portable terrestrial Light Imaging and Range Detection (LIDAR) systems capture accurate 3D measurements of the urban environment with spatial resolutions on the order of 5 centimeters or less [Blais, 2004]. The 3D imaging capability of these systems is negated, however, by a lack of commercial software tools capable of exploiting terrestrial LIDAR datasets [Shiode 2001]. Current approaches for creating high-resolution 3D urban models are expensive requiring thousands of man-hours to digitize feature geometries, assign textures to features and attribute features. The lack of robust AFE software tools for collecting geospecific urban features from terrestrial LIDAR systems directly impacts applications for facility reconnaissance, special operations planning and urban warfare decision-making. Visual Learning Systems, Inc. (VLS) has developed a LIDAR AFE system capable of extracting over 1,000 buildings per minute as 3D Shapefiles from airborne LIDAR. VLS' proven capability in developing commercial AFE software tools, and prior research experience with LIDAR, provides the Army with a proven partner for developing a 3D AFE solution for advanced terrestrial LIDAR systems

The U.S. Army requires automated feature extraction (AFE) software to collect 3D urban features from terrestrial LIDAR data in a timely and cost-effective manner to support the ground-based Warfighter operating in the urban battlespace. Vehicle-mounted terrestrial LIDAR systems have the capability to capture very-accurate 3D measurements of the urban environment with spatial resolutions on the order of 5 centimeters or less. The 3D imaging capability of these collection systems is negated, however, by a lack of commercial software tools capable of exploiting terrestrial LIDAR datasets with a high degree of automation. Current approaches for creating high-resolution 3D urban models are expensive, requiring thousands of man-hours to digitize feature geometries, assign textures to features, and then attribute features. Visual Learning Systems, Inc. has developed a unique capability to automatically extract 3D urban features from terrestrial LIDAR data including building facades, doors, windows, poles, trees, vehicles and other urban features of interest. The proposed Ground-Based 3D Urban Modeling Toolkit, designed as a plug-in for VLS’ Feature Analyst and LIDAR Analyst software, provides the Army with a comprehensive and automated 3D feature extraction solution for urban environments.

Keywords:
LIDAR, AUTOMATED FEATURE EXTRACTION, 3D URBAN MODELING, GIS, REMOTE SENSING