In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road ...In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road data.Secondly,several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network,to complete the N∶1(N>1)and 1∶1 matching.Thirdly,meshes of the two different scale road data with M∶N(M>1,N>1)matching relationships will be matched.Finally,roads will be classified into two categories under the constraints of meshes:mesh boundary roads and mesh internal roads,and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships.The results show that roads of different scales will be more precisely matched using the proposed method.展开更多
Matching multi-scale road networks in the same area is the first step in merging two road networks or updating one based upon the other.The quality of the merge or update depends greatly on the matching accuracy of th...Matching multi-scale road networks in the same area is the first step in merging two road networks or updating one based upon the other.The quality of the merge or update depends greatly on the matching accuracy of the two road networks.We propose an improved probabilistic relaxation method,considering both local and global optimizations for matching multi-scale of road networks.The aim is to achieve local optimization,as well as to address the identification of the M:N matching pattern by means of inserting virtual nodes to achieve global optimization effects.Then,by adding two attribute-related evaluation indicators,we developed four evaluation indicators to evaluate the matching accuracy,considering both geographic and attribute information.This paper also provides instructions on how to identify the proper buffer threshold during matching procedures.Extensive experiments were conducted to compare the proposed method with the traditional approach.The results indicate that:(1)the overall matching accuracy of each evaluation indicator exceeds 90%;(2)the overall matching accuracy increases by 6–12%after an M:N matching pattern is added,and by 4–6%following the addition of topology indicators;and(3)the proper buffer threshold is about twice the average value of the closest distance from all nodes.展开更多
基金The National Natural Science Foundation of China(Nos.4110136241471386)。
文摘In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road data.Secondly,several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network,to complete the N∶1(N>1)and 1∶1 matching.Thirdly,meshes of the two different scale road data with M∶N(M>1,N>1)matching relationships will be matched.Finally,roads will be classified into two categories under the constraints of meshes:mesh boundary roads and mesh internal roads,and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships.The results show that roads of different scales will be more precisely matched using the proposed method.
基金This work was supported by the National Natural Science Foundation of China[grant number 41371375]the Natural Science Foundation of Beijing Municipality[grant number 8132018]International Exchange and Joint Training Program of Graduate School of Capital Normal University.
文摘Matching multi-scale road networks in the same area is the first step in merging two road networks or updating one based upon the other.The quality of the merge or update depends greatly on the matching accuracy of the two road networks.We propose an improved probabilistic relaxation method,considering both local and global optimizations for matching multi-scale of road networks.The aim is to achieve local optimization,as well as to address the identification of the M:N matching pattern by means of inserting virtual nodes to achieve global optimization effects.Then,by adding two attribute-related evaluation indicators,we developed four evaluation indicators to evaluate the matching accuracy,considering both geographic and attribute information.This paper also provides instructions on how to identify the proper buffer threshold during matching procedures.Extensive experiments were conducted to compare the proposed method with the traditional approach.The results indicate that:(1)the overall matching accuracy of each evaluation indicator exceeds 90%;(2)the overall matching accuracy increases by 6–12%after an M:N matching pattern is added,and by 4–6%following the addition of topology indicators;and(3)the proper buffer threshold is about twice the average value of the closest distance from all nodes.
