High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a road boundary extraction technique that combines information from aerial color image and Light Detection And Ranging (LIDAR) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LIDAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadowpath reconstruction algorithms. After that, road boundary extraction is implemented by segmentation, edge detection, and edge linking method. Finally, road boundary lines are extracted as vector data by vectorization technique. The experimental results show that this approach is effective and great potential advantages.