A Geometric Attribute Collaborative Method in Multi-Scale Polygonal Entity Matching Scenario: Integrating Sentence-BERT and Three-Branch Attention Network

The cross-scale fusion and consistent representation of cross-source heterogeneous vector polygon data are fundamental tasks in the field of GIS, and they play an important role in areas such as the refined management of natural resources, territorial spatial planning, and the urban emergency response. However, the existing methods suffer from two key limitations: the insufficient utilization of semantic information, especially non-standardized attributes, and the lack of differentiated modeling for 1:1, 1:M, and M:N matching relationships. To address these issues, this study proposes a geometric–attribute collaborative matching method for multi-scale polygonal entities. First, matching relationships are classified into 1:1, 1:M, and M:N based on the intersection of polygons. Second, geometric similarities including spatial overlap, size, shape, and orientation are computed for each relationship type. Third, semantic similarity is enhanced by fine-tuning the pre-trained Sentence-BERT model, which effectively captures the complex semantic information from non-standardized descriptions. Finally, a three-branch attention network is constructed to specifically handle the three matching relationships, with adaptive feature weighting via attention mechanisms. The experimental results on datasets from Tunxi District, Huangshan City, China show that the proposed method outperforms the existing approaches including geometry–attribute fusion and BPNNs in precision, recall, and F1-score, with improvements of 3.38%, 1.32%, and 2.41% compared to the geometry–attribute method, and 2.91%, 0.27%, and 1.66% compared to BPNNs, respectively. A generalization experiment on Hefei City data further validates its robustness. This method effectively enhances the accuracy and adaptability of multi-scale polygonal entity matching, providing a valuable tool for multi-source GIS database integration.