LOCATION DISCOVERY OF VECTOR DATA UPDATE DRIVEN BY MAP IMAGE TILES FOR NATIONAL GEO-INFORMATION SERVICE PLATFORM

With the popularization of geographic information data applications, new requirements are put forward for the rapid update of vector data. The overall update of vector data is expensive and time-consuming. Therefore, we need to use various technical means to intelligently sense the changes of geographical entities and realize the active monitoring of the changes of vector data. In this paper, the building layers that are closely related to human beings and gradually become active geographic entities with the urbanization process are selected to monitor the location of the vector data to be updated. This paper first trains the model using single building layer tiles and image tiles. Then, based on the trained model, the location where the building layer tiles are inconsistent with the image tiles is found in the area to be detected. According to different situations, we set different thresholds to find the position to be updated in the vector data. After manual discrimination, the overall accuracy of the method proposed in this paper is 89%. This paper provides new insights into the update discovery of vector data. In addition, by further improving the boundary accuracy of extracted buildings, the extracted building results can be directly applied to the fusion update of vector data.