Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic object extraction from aerial imagery—a survey focusing on buildings
Computer Vision and Image Understanding
Feature-Preserving Medial Axis Noise Removal
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Representing Edge Models via Local Principal Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
A solution to the Path Planning problem using angle preprocessing
Robotics and Autonomous Systems
Vectorization of gridded urban land use data
Proceedings of the 2010 Workshop on Procedural Content Generation in Games
A knowledge-based problem solving method in GIS application
Knowledge-Based Systems
A fast algorithm for constructing approximate medial axis of polygons, using Steiner points
Advances in Engineering Software
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In this paper, a new method in order to achieve the geometrical and topological definition of extracted road networks is presented. Starting from a raster binary image where a road network is depicted, this algorithm seeks the automatic raster - vector conversion based on skeleton extraction and graph theory and using GIS database if it is available. The last goal of this method is to provide a numerical structured file which includes the geometric definition for all roads as well as the topologic relations between them. The applied technique comprises six steps. In the first step, the quality of the binary image is improved through a noise cleaning process. In the second step, parallel edges of road network are smoothed by means a generalization process. In the third step, skeleton is extracted applying a known and efficient method which some years ago was published. The fourth step consists in constructing the graph and generating the different cartographic objects which compose the road network. In this phase, GIS information can be used in order to improve the result. In the fifth step, objects are numerically adjusted by means of polynomial adjustment in the opened objects case, and using a reiterative polygonal adjustment in the sharp objects case. In the last step, mathematical morphology is applied to validate topologically the geometrical adjustment. For it, the junction nodes are analyzed for changing automatically their coordinates in order to achieve a topologically correct road network vectorization. Finally, objects are structured according to cartographic criteria and a numerical file with the vectorized road network is provided. Experimental results show the validity of this approach.