Road network generation method and device, readable storage medium and electronic equipment

Abstract

The invention relates to a road network generation method and device, a readable storage medium and electronic equipment. The method comprises the steps of obtaining multiple pieces of target trajectory data of a target area; according to the multiple pieces of target trajectory data, a target road grid map of the target area is predicted through a road prediction model, the road prediction model is obtained through training based on multiple pieces of sample trajectory data of the first area and sample road network data of the second area, and the target area, the first area and the second area are different from one another; and performing vectorization processing on the target road grid map to obtain target road network data of the target area. Therefore, unsupervised training of the road prediction model can be realized, and the road network data in one-to-one correspondence with the trajectory data does not need to be marked, so that the model training cost is reduced, and the model training efficiency is improved. In addition, the problem that the model prediction accuracy is affected due to the fact that the quality of manually annotated data cannot be guaranteed can be avoided, and therefore high-quality road network data can be obtained.

Description

technical field [0001] The present disclosure relates to the field of computer technologies, and in particular, to a road network generation method, apparatus, readable storage medium, and electronic device. Background technique [0002] Roads are important infrastructures connecting different places and play an irreplaceable role in the production and life of society. In the digital age, road network data is the key support for building a smart city system. With the popularization of devices equipped with Global Positioning System (GPS) and the wide application of Location Based Services (LBS), various organizations apply traditional algorithms or supervised training based on massive trajectory information. The deep learning model builds the road network and exports the road network data. Among them, the deep learning model obtained based on supervised training needs to label the road network data corresponding to the GPS trajectory one-to-one in the training stage, and t...

AI Technical Summary

Problems solved by technology

Among them, the deep learning model based on supervised training needs to label the road network data that corresponds to the GPS track one-to-one during the training phase. Manual labeling is costly and inefficient

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