Road network matching method combining human interaction and active learning

Abstract

The invention provides a road network matching method combining artificial interaction and active learning. The method comprises the following steps: carrying out data preprocessing on original road network data to obtain road network information; screening out candidate matching roads through buffer area analysis, and constructing an initial matching probability matrix according to the geometric difference value between the two roads; updating the initial matching probability matrix to enable the road matching to be globally optimal; calculating an entropy value of a road matching probability through a query function, selecting a road which is not marked with a matching object and has the maximum entropy value, and handing over the road to a worker for processing, and constructing a matching constraint set; and finally, based on a matching constraint set of human interaction, optimizing a probability relaxation matching result, and improving the final road matching precision. According to the method, the precision of road network matching can be obviously improved through an active learning method, the problem of wrong matching caused by too high road structure similarity in complex road network matching by using a probabilistic relaxation algorithm is solved, a robust matching pair can better conform to the actual situation, and matching is more accurate.

Description

technical field [0001] The invention relates to a road network matching method combining manual interaction and active learning, and belongs to the technical field of spatial data integration. Background technique [0002] Spatial data integration refers to the process of matching, transforming and comprehensively processing multi-source spatial data. With the development and integration of 3S technology (GIS, RS, GPS), the spatial vector data obtained by different means has increased rapidly, and various data have differences in accuracy, scale, and semantic expression, resulting in repeated collection of spatial data. , leading to difficulties in data sharing and untimely updates. How to quickly and efficiently integrate and process multi-source, multi-dimensional heterogeneous spatial data, and improve the value and utilization of data has become an important topic in the field of geographic information science. [0003] Object matching is one of the key technologies of...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the deficiencies of the prior art, the present invention provides a road network matching method that combines manual interaction and active learning, which can make full use of the geometric features and topological relationships of the roads to build a probabilistic relaxation model for road matching, and continuously Train the model to improve road matching accuracy and overcome problems such as wrong matching caused by complex road structures and high similarity between different arcs

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