From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL

TL;DR

This paper presents a multi-agent framework that improves spatial Text-to-SQL translation accuracy by decomposing the task into specialized stages, enhancing robustness for spatial queries.

Contribution

The novel multi-agent approach effectively addresses spatial semantics and schema ambiguities, achieving higher accuracy on spatial Text-to-SQL benchmarks.

Findings

Achieved 81.2% accuracy on KaggleDBQA benchmark.

Achieved 87.7% accuracy on SpatialQueryQA benchmark.

Decomposition into agents improves robustness for spatial queries.

Abstract

The complexity of SQL and the spatial semantics of PostGIS create barriers for non-experts working with spatial data. Although large language models can translate natural language into SQL, spatial Text-to-SQL is more error-prone than general Text-to-SQL because it must resolve geographic intent, schema ambiguity, geometry-bearing tables and columns, spatial function choice, and coordinate reference system and measurement assumptions. We introduce a multi-agent framework that addresses these coupled challenges through staged interpretation, schema grounding, logical planning, SQL generation, and execution-based review. The framework is supported by a knowledge base with programmatic schema profiling, semantic enrichment, and embedding-based retrieval. We evaluated the framework on the non-spatial KaggleDBQA benchmark and on SpatialQueryQA, a new multi-level and coverage-oriented benchmark with diverse geometry types, workload categories, and spatial operations. On KaggleDBQA, the system reached 81.2% accuracy, 221 of 272 questions, after reviewer corrections. On SpatialQueryQA, the system achieved 87.7% accuracy, 79 of 90, compared with 76.7% without the review stage. These results show that decomposing the task into specialized but tightly coupled agents improves robustness, especially for spatially sensitive queries. The study improves access to spatial analysis and provides a practical step toward more reliable spatial Text-to-SQL systems and autonomous GIS.