Methods For Modeling Levels-of-Detail Road Networks With The Maintenance Of Structural Patterns

Road network data is a fundamental part of geospatial database representing the construction level of spatial information in our country. Moreover, it also supports the urban space locating service that provides essential spatial data for location based service, smart navigation, social network services, and so on. With the rapid development of spatial data acquisition technologies and spatial information services, the level-of-derail (LoD) modeling of road network is undergoing the evolution from storage by independent layers to some automatic generalization and integration techniques. Then, under the framework of automatic road network LoD modeling, the maintenance of structural pattern and the application of multi-source datasets matching are both being confronted with some new challenges. Existing algorithms, including some representation algorithms, is not able to solve the pattern maintenance problem in the simplification process. Meanwhile, for the objective of road network matching of multi-source datasets, the existing approaches do not support building the relations between the segments representing the same road object automatically under heterogeneous coordinate systems.Because of the shortage above, a LoD model of maintaining structural pattern in road simplification and matching road network by structural patterns is designed in this dissertation. In detail, the main research works of the dissertation are as follows:1) In the view of multi-resolution database construction, the research background of the dissertation is introduced. With the further development of information construction of surveying, the field of geospatial information is changed from traditional "database construction" to "spatial information services". In this dissertation we start from the recognition of several patterns in road network, and point out that it is necessary for the purpose of maintaining the characteristic of road networks. Meanwhile, the patterns can also be used to build relations between road features in multi-source datasets in order to reduce the discreteness and improve the consistence between spatial datasets.2) The concepts of spatial patterns, recognition methods and application fields are reviewed to the research status of spatial pattern recognition algorithms, which point out that pattern recognition technology has been the important and difficult point in the geospatial science. Then the dissertation proposed the LoD classification with three scales, which is necessary to be further researched for the purpose of LoD simplification and multi-source data matching. 3) The recognition and simplification approaches of three-level road network patterns are put forword. For local patterns, this dissertation first proposes a heuristic tracking approach for dual road pattern recognition on the basis of line feature, the rapid extraction approach on the basis of polygon feature, and the centerline extraction approach based on Delaunay triangles. Whereafter, the dissertation analyzes the shape characteristic of complex road junction pattern, and proposes the clustering approach for pattern recognition and simplification approach for shape and topology maintenance. For grid pattern, with the characteristic descriptors, an adaptive recognition approach of road grid pattern is proposed on the basis of shape similarity and regional growing algorithm. Based on that, a local grid pattern maintenance approaches are proposed to maximize the information amount in aggregation. Furthermore, a mixed integer programming model is proposed to maintain the global grid pattern characteristic. For global patterns, a stroke pattern extraction approach is first proposed based on the principle of Gestalt. The algorithms of stroke extraction with dual roads and the optimum connecting cross complex junction are designed to fulfill the needs of "continuous connecting". Based on that, a hierarchical road selection algorithm with complex network analysis is proposed to detection the skeleton pattern in macro scale, and the connection topology is maintained using minimum spanned tree (MST) concept.4) Several experiments show the pattern recognition results, the maintenance of local, regional and global patterns, the comparison between structural hierarchy and construction hierarchy, and the synthesized global and regional pattern maintenance. They proved the validation of the model and the algorithm discussed above.5) An application of pattern-based matching algorithm for multi-source datasets is proposed to deal with different coordinate systems, which is common in heterogeneous datasets. Three functional descriptors including local, regional and global structures are defined as local network, maximum common sub network, and so on. Based on these, the global robust registering and matching algorithms are proposed to achieve the purpose of multi-source road data matching.