Hierarchical stroke mesh: a new progressive matching method for detecting multi-scale road network changes using OpenStreetMap

Abstract

Spatial feature matching is the key to detecting incremental changes in spatial data and extracting the updated information. The accuracy of spatial feature matching can depend on the structural organization of the data being compared; inconsistent data structures make comparison more difficult. OpenStreetMap (OSM) road network data, for example, is updated frequently to the point of being unstable, making the matching process used in information extraction susceptible to interference. To use OSM for comparison with other road data sources, this problem must be addressed. This paper proposes a new multi-scale dynamic matching algorithm based on a hierarchical stroke mesh (HSM) to detect matches between OSM data and professional surveying and mapping data and to update the change information. By improving the integrity and continuity of the stroke generation method and its algorithm for evaluating the importance of information, the algorithm proposed in this paper identifies the spatial hierarchy contained in the road network and abstracts the road network. The result is the HSM. The algorithm is based on multi-scale matching constraint rules designed from coarse to fine in terms of both resolution and granularity. It is used to detect one-to-one or one-to-many mapping relationships among different mesh levels (mesh, mesh boundary segment, and mesh inner segment). This allows progressive iterative matching between the older survey data and the newer OSM data. The results show that the HSM algorithm proposed in this paper can detect incremental changes between the two vector data sources quickly and accurately. Compared with others, this algorithm can effectively improve matching accuracy while sacrificing little performance.