Research On Multi-scale Road Network Vector Data Matching Method

Vector spatial data is an important part of geographic information,and also the main carrier of human and nature information.It plays a very important role in national economy and national defense modernization.With the rapid progress and development of geographic information science,different departments have produced a large number of spatial data based on different needs,and there are some differences in scale,tense and other aspects.In practical applications,in order to reduce the cost of data acquisition and maximize the use of existing data,data processing and updating are needed.One of the key technologies is multi-scale vector space data matching,which has not been perfected and mature,and is currently in the bottleneck period of research,so further exploration is needed.Multi-scale vector spatial data matching can be divided into geometric matching,topological matching and semantic matching.Most of the matching methods currently applied in practice are mainly based on geometric and topological features.Due to the poor applicability of many similarity indicators in cross-scale data,the accuracy of object recognition is reduced.Therefore,in order to improve the accuracy and efficiency of road network matching,this paper takes multi-scale road network vector data as an example to study the matching method based on geometric and topological features,and the main achievements are as follows:Firstly,the existing similarity feature description methods are analyzed,and three similarity feature descriptors are proposed.Based on the analysis of the different representation types of multi-scale road entities,this paper introduces the similarity feature description methods and their calculation principles from the aspects of distance,shape,direction,length,topology and semantics,and analyzes their advantages and disadvantages under different matching conditions.In order to reduce the matching fuzziness of multi-scale road network data and improve the matching accuracy,three metrics suitable for vector road matching are proposed based on road geometry and topological features.These are the summation product based on orientation and distance(SOD),the area shape descriptor based on the minimum convex hull,and the shape descriptor based on the feature point vector.The construction principle and calculation method of three kinds of measurement indexes are described in detail.Secondly,the conceptual model and process of road network matching are designed,and the screening method of the existing candidate matching data sets is optimized: For the data set with large scale span,the small scale data set was taken as the reference data set,and the road matching unit was constructed by generating the section Tyson polygon.The intersection of the small scale data set and the large scale data set was performed for the nearest neighbor analysis,and the fine matching candidate set was obtained.Finally,different models in this paper are programmed and realized based on Python language.According to the road network matching process designed in this paper,the proposed three shape descriptors are compared and verified by four models,and the effectiveness of the proposed three shape descriptors is proved respectively.And through seven kinds of similarity index to build integrated entity matching similarity matching model,results show that: in this paper,the improved selection method of the matching of the candidate set significantly reduced the error matching,leakage rate,the proposed index than other index more accurately describe the geometric characteristics of the entity under different scale,improves the matching speed and accuracy.