标题: A Multi-Scale Hybrid Scene Geometric Similarity Measurement Method Using Heterogeneous Graph Neural Network

作者: Gong, CY (Gong, Chongya); Ai, TH (Ai, Tinghua); Chen, SY (Chen, Shiyu); Xiao, TY (Xiao, Tianyuan); Yu, HF (Yu, Huafei)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION  卷: 14  期: 2  文献号: 84  DOI: 10.3390/ijgi14020084  Published Date: 2025 FEB  

摘要: Geographic features in maps consist of a mixture of points, polylines, and polygons, generally including POIs, roads, buildings, and other geographic features. Due to the differing dimensionality of these various types of geographic data, traditional geometric similarity measurement methods that rely on a single type of feature are not applicable to mixed scenes. The traditional solution to this issue is to treat points as projections of polylines and polylines as projections of polygons. Through neural networks, projection matrices can be learned to convert points, polylines, and polygons into the same type of object, thereby enabling the use of single-scene geometric measurement methods (e.g., Graph Neural Networks) to solve the problem. However, the key challenge in using Graph Neural Networks for similarity measurement is learning the adjacency relationships between geometric features. It is evident that the adjacency relationships between different feature pairs, such as polyline-polygon, polyline-polyline, and polygon-polygon, require different approaches for measurement, and these diverse relationships cannot be captured by a simple GNN. Heterogeneous Graph Neural Networks (HGNNs) are suited to address this problem: different adjacency relationships between feature pairs can be learned using distinct embedded networks, the new node characteristics can be calculated through the information aggregation and propagation framework of HGNNs, and these new characteristics can be used for geometric similarity measurement. Finally, the effectiveness of the proposed method was verified through practical experiments.

作者关键词: heterogeneous graph neural network; hybrid scene; similarity measurement; data-driven

KeyWords Plus: FEATURES

地址: [Gong, Chongya; Ai, Tinghua; Xiao, Tianyuan; Yu, Huafei] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Chen, Shiyu] Xinyang Normal Univ, Sch Geog Sci, Xinyang 464300, Peoples R China.

通讯作者地址: Ai, TH (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: chongyagong@gmail.com; tinghuaai@whu.edu.cn; csy_hy@xynu.edu.cn; xiaotianyuan@whu.edu.cn; huafeiyu@whu.edu.cn

影响因子：2.8