Multi-Scale Road Network Similarity Calculation Method
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Solution Overview
Problem
Existing methods for calculating spatial similarity in road networks fail to integrate semantic information effectively, leading to incomplete similarity assessments across different scales.
Innovation Solution
A multi-feature road network spatial similarity calculation method that considers road network skeleton lines and local hierarchy multi-features, calculating similarity step-by-step from integral skeleton lines to local details, incorporating topological, geometric, and semantic features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calculation methods focus on topology, geometry and shape features respectively, then the calculation process is simple, but the similarity measurement is incomplete and cannot integrate semantic information well
Solution Approach 1:
The patent segments the similarity calculation into three distinct modules: skeleton line similarity calculation (capturing global structure), road mesh similarity calculation (capturing topological relationships), and local detail feature similarity calculation (capturing semantic information). This segmentation allows each module to focus on specific aspects while maintaining overall completeness.
Solution Approach 2:
The patent merges multiple feature types (skeleton line features, road mesh topological features, and local detail semantic features) into a unified multi-scale similarity calculation model. The final similarity score integrates results from all three modules, achieving comprehensive measurement that combines structural, topological, and semantic information.
2Measurement precision
If attention is paid to both overall skeleton structure and local detail features, then the similarity determination is more comprehensive, but the calculation complexity increases
Solution Approach 1:
The patent performs preliminary extraction of skeleton lines and road meshes before the actual similarity calculation. This preprocessing step organizes the data into structured formats that facilitate efficient comparison, reducing the computational burden during the similarity assessment phase.
Solution Approach 2:
The patent introduces a multi-scale dimensional framework that operates at different levels: global scale (skeleton lines), meso scale (road meshes), and local scale (detail features). This dimensional approach allows simultaneous consideration of multiple features without linearly increasing complexity, as each dimension can be processed independently and then integrated.
Data Source
AI summary
Disclosed in the present disclosure is a similarity calculation method considering multiple features for a multi-scale road network, which includes structural similarity calculation of skeleton lines and local feature similarity calculation of the multi-scale road network. A road network stroke is generated, skeleton lines of the road network are extracted and transformed into a structure tree, and the skeleton similarity of the road network is evaluated by calculating structural similarity of the structure tree of the multi-scale road network. Topological similarity of the multi-scale road network is calculated by using a difference matrix of a conceptual domain graph of road meshes, geometric similarity of the multi-scale road network is calculated by using a density of road meshes, and local similarity of the multi-scale road network is obtained by integrating the hierarchy of the road network into the topological and geometric similarity calculation in the form of matrices.