A Unified Framework for Temporal Similarity: Coupling Time-Scale Effects and Composite Temporal Objects in Geoscientific Retrieval

Temporal similarity is critical for retrieving and integrating geoscientific data. Existing approaches often neglect the effect of time scale and lack efficient models for handling composite temporal objects composed of multiple points and intervals. To address these limitations, we propose a unified temporal similarity framework that explicitly incorporates both time-scale effects and composite temporal topology.To evaluate the scale-sensitive model, we compile the Geoscientific Data Retrieval and Ranking Dataset (GDRRD), containing 105 datasets organized into themed query groups with expert rankings. On GDRRD, our method improves the Spearman correlation between system ranking and expert ranking (from 0.905 to 0.976 compared with a representative baseline) and shows superior discrimination for disjoint relations under decade-scale and century-scale settings.To extend the framework to composite time, we introduce a sparse matrix representation based on temporal transitivity and a most-similar matching model that aggregates topological and metric similarities with length-weighted integration. Experiments on composite-time cases derived from gridded population density datasets (2000–2015, 1 km resolution) and remote sensing image sequences demonstrate that our method reduces redundant relation storage by over 40%, while yielding more precise similarity scores for complex temporal queries such as “2000, 2005, 2010” versus “2000–2010”.By jointly achieving scale-awareness and form-completeness, the proposed framework provides a robust foundation for temporal similarity computation, enabling more accurate and rational retrieval in large-scale geoscientific data repositories.