Automatic Extraction of Number of Lanes from Georectified Aerial Images

The number of lanes is a basic input to many transportation studies. Traditionally, these data are either collected in the field or manually extracted from aerial images. These methods of data acquisition are both resource intensive and time-consuming, especially when large study areas are involved. The availability of remotely sensed georectified aerial images provides an inexpensive alternative to acquiring these data via automatic feature extraction. This paper explored a method to automatically extract the number of lanes from high-resolution georectified aerial images. In this method, geographic coordinates of a target roadway were precisely mapped to pixels in aerial images. These pixels were first grouped into fixed-length profiles. The saturation threshold was then applied to exclude profiles covering nonpaved areas such as plants and building roofs. Lane profile candidates were then identified from the remaining profiles with the support vector machine classification technique. The number of lanes was then estimated by optimizing the lane locations according to the lane profile candidates and lane width constraints. The method was tested by using georectified aerial images of Miami–Dade County in Florida. For all six test cases involving two-lane and four-lane suburban roadway segments, the method accurately determined the number of lanes. Test results indicate that it is feasible to extract the number of lanes from georectified aerial images in suburban and urban areas with the method developed in this research.