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AUTOMATIC COMPILATION OF 3D ROAD FEATURES USING LIDAR AND MULTI-SPECTRAL SOURCE DATA

While many commercial cartographic feature extraction systems process panchromatic and color imagery, few systems fully integrate airborne LIDAR data as a component of the feature extraction process. Most computational techniques seek elevation discontinuities at object boundaries or use the physics of the LIDAR signal to detect regions indicative of sharp edges and/or foliage. Automated road extraction presents a particular problem for these techniques because the height of the road surface is not significantly different from the height of the surrounding terrain. Our approach is to use co-registered airborne LIDAR and multi-spectral source (MSS) data as additional evidence for evaluation and extrapolation of road surfaces and centerlines within a commercial road network extraction system. Thus, road tracking provides the local context for source data processing in a relatively narrow search area by using LIDAR/MSS data to contribute to feature detection and delineation. LIDAR can provide the system with a direct determination of road height, pitch, and slope, while MSS can be used to estimate surface material properties. This work builds on previously reported work (Harvey, et al. 2006) that used multiple panchromatic image views to derive road elevation and slope while performing feature extraction. Incorporating co-registered LIDAR permits direct elevation determination and is less sensitive to occlusions and multi-image matching errors. In this paper we describe results in detection and delineation of road networks using co-registered LIDAR/MSS imagery. In addition to further automating the extraction process, we show that this approach improves road network accuracy and completeness, especially in complex urban environments.