Multi-scale polygons matching using a new geographic context descriptor

Abbaspour, Rahim Ali; Chehreghan, Alireza; Chamani, M.

doi:10.1007/s12518-021-00396-x

Cited by 2 publications

(1 citation statement)

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“…As spatial data are becoming abundant, improving both the reuse and the quality of existing spatial data has become more important than ever. There is now an urgent need to develop approaches for spatial data integration and updating, and object matching has become one of the most productive solutions for data integration and updating between multi-scale maps [2,3]. Object matching is based on the similarity of geometry, topology, and semantics between multi-scale objects [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors

Sun,

Lu,

Ding

et al. 2023

IJGI

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Most commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By considering the sources of various scales and landmark datasets, as well as the spatial relationships between the selected objects and the detailed features of the entities, we propose an improved matching metric, the summation product of orientation and distance (SOD), combined with the shape descriptor based on feature point vectors, the shape area descriptor based on the minimum convex hull, and three other indicators, to establish multiple multi-scale road matching models. Through experiments, the comprehensive road matching model that combines SOD, orientation, distance and length is selected in this paper. When matching the road dataset with a scale of 1:50,000 and 1:10,000, the precision, recall, and F-score of the matching result of this model reached 97.31%, 94.33%, and 95.8%, respectively. In the case that the scale of the two datasets did not differ much, we concluded that the model can be used for matching between large-scale road datasets.

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Section: Introductionmentioning

confidence: 99%