Multimodal and Multiscale Spatial-Temporal Semantic Search and Recommendation with AI Foundation Models
Semantic search and recommendation of similar documents, such as news and reports about unusual environmental events (e.g., a dead whale washed ashore in Alaska) that contain spatial and temporal information, is a critical task in Geographic Information Retrieval (GIR). This work presents a novel framework that leverages AI foundation models, including Large Language Models (LLMs) and Vision-Language Models (VLMs), to enable effective similarity search and ranking for such event documents. To support this goal, we introduce two new strategies: (1) CAMERA (Context-Aware Multimodal Event Retrieval Algorithm), which fuses textual and visual information to generate richer embeddings than thos...
| Authors | Yuanyuan Tian, Wenwen Li, Xiao Chen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 15 June 2026 |
| Last updated at source | 30 June 2026 |
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