A triplet-loss optimized dual-branch neural network for coupled spatial-attribute similarity measurement of vector data

Vector-data similarity (VD_SM) quantifies the similarity between objects’ spatial and attribute characteristics and underpins retrieval, recommendation, and infringement detection. Fundamental differences in the representation of spatial and attribute features challenge coupled VD_SM measurement, and existing methods rarely integrate both aspects. This study proposes a coupled VD_SM computed through a dual-embedding network optimized with triplet loss. Triplet-ResNet101 learns spatial embeddings from vector images, and Triplet-AttTabNet learns attribute embeddings from attribute tables. Triplet loss projects both embeddings into a shared metric space in which similar features are positioned closer than dissimilar ones. The spatial and attribute embeddings are concatenated, and VD_SM is computed as the Euclidean distance between the concatenated vectors. Experimental results show that (1) Triplet-ResNet101 and Triplet-AttTabNet achieve triplet ranking accuracies of 99.09% and 98.32%, respectively, both surpassing baseline methods; and (2) VD_SM attains an average retrieval accuracy of 92.74%, while a questionnaire yields 90% satisfaction. These results demonstrated that the triplet-loss-based dual-embedding framework provides an effective approach for jointly measuring spatial and attribute similarity in vector data.