The use of on-road methods to accurately describe the behaviours occurring in everyday real-world conditions, to quantify risks for safety critical events, and an improved understanding of the factors that contribute to risk, clearly has huge potential to promote further road trauma reductions.
Hence, road extraction method that is significantly more automated has become a research hotspot in remote sensing information processing.
Results show that this approach provides a rapid and efficient 3D road modeling method for applications that have stringent requirements on high-fidelity road models.
Automatic road extraction method using road intersection model is presented by Boshir et al. [27].
The remaining part of this paper consists of Section 2 which discusses about the methodology of proposed road extraction method.
Zelang et al. presented road extraction method based on tensor voting, kernel density estimation, and subspace constrained mean shift [21].
Wenzhong et al. presented road extraction method for urban area which includes two stages; image is classified as road class and non-road class using path openings and closings, and road images are refined by shape features.
The major problem of road extraction method is the complex structure of the images, which contain many different objects, such as roads, houses, trees, vehicles, etc., with differences in shape, tone, and the texture.
Dal et al. proposed an automatic road extraction method which included two steps: first step is road seed extraction-based geometric and radiometric properties of an image, and the next step is linking of road seeds based on two basic perceptual grouping rules, the proximity and collinearity rules [6].
Finally, as current road construction methods require a large amount of maintenance, any improved construction methods including bio-engineered roads with still require maintenance yet to a lesser extent (Howell, 1999).
