Abstract
With the rapid development of three-dimensional point cloud acquisition from mobile laser scanning systems, the extraction of urban roads has become a major research focus. Although it has great potential for digital image processing, the extraction of roads using the region growing approach is still in its infancy. We propose an automated method of urban road extraction based on region growing. First, an initial seed is chosen under constraints relating to the Gaussian curvature, height and number of neighboring points, which ensures that the initial seed is located on a road. Then, the growing condition is determined by the angle threshold of the tangent plane of the seed point. Then, new seeds are selected based on the identified road points and their curvature. The method also includes a strategy for dealing with multiple discontinuous roads in a dataset. The result shows that the method can not only achieve high accuracy in urban road extraction but is also stable and robust.
Highlights
Urban roads, as a major element of a city’s infrastructure, have always been important in the life of city dwellers, because people’s business and pleasure can be connected through them [1]
In order to solve the problem of creating automatic road extraction technology when there is only spatial coordinate information in the mobile laser scanning (MLS) data, in this paper, we propose an automated method of road extraction based on region growing
The results show that the method can achieve a high accuracy in urban road extraction but is robust
Summary
As a major element of a city’s infrastructure, have always been important in the life of city dwellers, because people’s business and pleasure can be connected through them [1]. The accurate extraction of roads can provide important support for road surveying, autonomous vehicle navigation, and other applications [2]. Because it employs a non-contact approach and has a high speed and accuracy, three-dimensional (3D) laser scanning has become one of the most effective measurement techniques, and using 3D point cloud data to extract urban features has become popular in research [4]. The sources of 3D point cloud data include airborne laser scanning (ALS), terrestrial laser scanning (TLS) and mobile laser scanning (MLS). MLS, which is faster than TLS in terms of acquisition speed and higher than ALS in terms of data resolution, has been widely used in route planning, road design and road surveying [5]
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