Determining the optimal generalization operators for building footprints using an improved graph neural network model

Abstract

Determining the optimal generalization operators of city buildings is a crucial step during the building generalization process and an important aspect of realizing cross-scale updating of map data. It is a decision-making behavior of the cartographer that can be learned and simulated using artificial intelligence algorithms. Multi-scale data can provide rich generalization samples to train the determination process. However, previous studies have focused primarily on the intelligent use of each generalization operator separately, neglecting the intelligent scheduling issue between multiple operators when they are used simultaneously. Herein, we propose an improved graph neural network (GNN) called self-neighborhood merged GNN (SNGNN) that selects the optimal generalization operators for different buildings. In SNGNN, node and edge information are passed with different weights through two modules to simulate the effects of a building on itself and the neighborhood on either side. SNGNN has been experimentally validated using sample datasets for Ningbo, China, at 1:10,000 and 1:25,000. The F1-score of the testing dataset was 94.19%, and the classification precision of each operator was ≥87%. Compared with other popular intelligent algorithms, the experimental results for SNGNN revealed better performance in determining the optimal generalization operators.

Keywords:

• Cartographic generalization
• generalization operator
• decision-making
• graph neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data and codes that support the findings of this study are available with the identifier(s) at the private link: https://doi.org/10.6084/m9.figshare.23118758.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [number 42271463, 42101453, 42371461] and the Natural Science Foundation for Distinguished Young Scholars of Henan Province [number 212300410014].