Accurate extraction and reconstruction of route corridor features from geospatial data is a prerequisite to effective management of road networks for engineering, safety and environmental
applications. High quality road geometry and road side features can now be extracted from
dense point cloud LiDAR data, recorded by modern day Mobile Mapping Systems. This valuable
route network information is gaining the attention of road safety and maintenance engineers.
Road points are needed to be correctly identified, classified and extracted from LiDAR data
before reconstructing intrinsic road geometry and road-side infrastructure. In this paper, we
present a method to automatically extract the road from terrestrial based mobile laser scanning
system using the GVF (Gradient Vector Flow) snake model. A snake is an energy minimizing
spline that moves towards the desired feature or object boundary under the influence of internal
forces within the curve itself and external GVF forces derived typically from 2D imaging data by
minimizing certain energy such as edges or high frequency information. In our novel method, we
initialise the snake contours over point cloud data based on the trajectory information produced
by the MMS navigation sub-system. The internal energy term provided to the snake contour is
based on adjusting the intrinsic properties of the curve, such as elasticity and bending, whilst
the GVF energy and constraint energy terms are derived from the LiDAR point cloud attributes.
Our method primarily differs from the traditional snake models in initialisation and in deriving the
energy terms from the 3D LiDAR data
