SpatCode: Rotary-based Unified Encoding Framework for Efficient Spatiotemporal Vector Retrieval

2026-01-14

AI Summary

This work proposes a unified spatiotemporal vector retrieval framework that effectively integrates temporal, spatial, and semantic information into a consistent similarity space, addressing the limitations of existing methods that rely on external filters or specialized indexes and struggle with multimodal heterogeneous data. The core innovations include a unified embedding representation based on rotary positional encoding, a recurrent incremental update mechanism under a sliding window, and a context-aware retrieval algorithm driven by interest-aware weights. Extensive experiments on multiple real-world datasets demonstrate that the proposed approach significantly outperforms state-of-the-art methods, achieving superior performance in retrieval accuracy, computational efficiency, and robustness under dynamic data evolution.

Technology Category

• Computer Vision: Image and Video Retrieval
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Spatiotemporal vector retrieval has emerged as a critical paradigm in modern information retrieval, enabling efficient access to massive, heterogeneous data that evolve over both time and space. However, existing spatiotemporal retrieval methods are often extensions of conventional vector search systems that rely on external filters or specialized indices to incorporate temporal and spatial constraints, leading to inefficiency, architectural complexity, and limited flexibility in handling heterogeneous modalities. To overcome these challenges, we present a unified spatiotemporal vector retrieval framework that integrates temporal, spatial, and semantic cues within a coherent similarity space while maintaining scalability and adaptability to continuous data streams. Specifically, we propose (1) a Rotary-based Unified Encoding Method that embeds time and location into rotational position vectors for consistent spatiotemporal representation; (2) a Circular Incremental Update Mechanism that supports efficient sliding-window updates without global re-encoding or index reconstruction; and (3) a Weighted Interest-based Retrieval Algorithm that adaptively balances modality weights for context-aware and personalized retrieval. Extensive experiments across multiple real-world datasets demonstrate that our framework substantially outperforms state-of-the-art baselines in both retrieval accuracy and efficiency, while maintaining robustness under dynamic data evolution. These results highlight the effectiveness and practicality of the proposed approach for scalable spatiotemporal information retrieval in intelligent systems.

Problem

• spatiotemporal retrieval
• vector search
• heterogeneous modalities
• temporal constraints
• spatial constraints

Innovation

• Rotary-based Encoding
• Spatiotemporal Retrieval
• Incremental Update
• Unified Representation
• Weighted Interest Retrieval