Research on multi-scale vector road matching model based on ISOD descriptor

In the data processing of geographic information, the matching of road data at different scales is crucial. Due to scale differences, road features can change, posing a challenge to multi-scale matching,.Spatial relationships are essential for multi-scale matching because they remain stable at different scales. In this paper, we propose an improved the summation product of orientation and distance (ISOD) descriptor, which combines features such as pinch chain code and curvature variance with similarity metrics, such as length, direction, and Hausdorff distance, to construct an integrated similarity metric model for multi-scale road matching. The experiments proved that the model achieved 94.75% and 93.34% check accuracy and completeness in road data matching at scales of 1:50,000 and 1:10,000. The model also achieved 86.39% and 94.06% check accuracy and completeness in road data matching at scales of 1:250,000 and 1:50,000, respectively. This proves the effectiveness and practicality of the method. The ISOD descriptor and the integrated similarity metric model in this paper provide an effective method for multi-scale road data matching, which aids the integration and fusion of geographic information data and is significantly valuable when applied in the fields of intelligent transport and urban planning.