SBIR-STTR Award

Under this Phase I SBIR, TerraSim, Inc. will develop a novel and robust road network extraction system tailored to the rapid processing of LIDAR data and co-registered reflective and multi-spectral imagery. RoadMAP(tm) from TerraSim, a single image semi-automated road detection and tracking system, will be modified to derive and incorporate height estimates from LIDAR and surface material estimates from multi-spectral data as new and integral components of the road network extraction process.

Benefit:
Under this Phase I SBIR, TerraSim, Inc. will develop a novel and robust road network extraction system tailored to the rapid processing of LIDAR data and co-registered reflective and multi-spectral imagery. RoadMAP(tm) from TerraSim, a single image semi-automated road detection and tracking system, will be modified to derive and incorporate height estimates from LIDAR and surface material estimates from multi-spectral data as new and integral components of the road network extraction process.

Keywords:
common geospatial database, common geospatial database, rapid urban database construction, 3D road network extraction, LIDAR processing

This Phase II proposal will address source data preparation for modeling and simulation and cartographic feature applications using semi-automated road network extraction technology. The primary goal is to use airborne LIDAR data to generate 3D road network geometry augmented with physical attribution. We will utilize RoadMAP, our road network extraction system, which currently performs local-area processing to extrapolate from a road starting point to delineate complete road features. Under this SBIR, we will incorporate both co-registered airborne LIDAR data to directly determine road height, pitch, and slope, and co-registered multi-spectral source (MSS) data to estimate surface material properties. By pre-processing LIDAR/MSS imagery to create a "traffickability map", automated road starting points will be detected that contain likely avenues for roads. Our Phase I results demonstrate that creating the traffickability map successfully classifies road-like features regardless of whether they are in flat, sloped, or undulating terrain. The map is created in world coordinates so that other geo-referenced data can easily be correlated and incorporated into RoadMAP's decision making modules. Additionally, a machine learning module will be integrated into RoadMAP to improve the level of automation within the interactive road extraction process.

Keywords:
3d Road Network Extraction, Lidar, Multi-Spectral, Data Fusion, Modeling And Simulation, Rapid Generation Of Geospatial Data, 3d Geospatial Visualizat