GRASP: Graph-Enhanced Retrieval for Accurate Schema Pruning in Text-to-SQL.

Recent advances in land system research depend heavily on efficient access to large-scale, multi-source remote sensing spatiotemporal databases. Although Text-to-SQL provides natural language interfaces, the scale and spatial complexity of remote sensing schemas generate significant noise for large language models, increasing inference costs and latency. This study presents graph-enhanced retrieval for accurate schema pruning (GRASP), a graph-based framework for schema pruning in remote sensing information systems. GRASP frames schema pruning as a semantic retrieval task and constructs a heterogeneous graph that represents both question semantics and database structure. By integrating a relation-aware transformer, a relational graph attention network, and pre-trained BERT representations, GRASP enhances schema understanding and supports joint table-column prediction through entity-level cross-attention. A dual-task objective combining contrastive learning with dynamic-threshold prediction mitigates class imbalance, while database value sampling and demonstration retrieval optimize inference performance. Experiments show that GRASP substantially improves schema pruning in spatiotemporal query scenarios: a 7B open-source LLM with GRASP surpasses an unaugmented 32B model on Spider; meanwhile, the framework also yields promising results on SpatialSQL, achieving a favorable balance among accuracy, cost, and deployment flexibility. GRASP provides a practical pathway for interdisciplinary researchers to query remote sensing databases in natural language, aiding spatiotemporal analysis.