In recent years, large language models (LLMs) have achieved remarkable progress in natural language understanding and structured query generation (NL2SQL). However, extending these advances to GeoSQL tasks in the PostGIS environment remains challenging due to the complexity of spatial functions, geometric data types, and execution semantics. Existing evaluations primarily focus on general relational databases or Google Earth Engine code generation, leaving a lack of systematic benchmarks tailored to spatial databases. To address this gap, this study introduces GeoSQL-Eval, the first end-to-end automated evaluation framework for PostGIS query generation. Built upon Webb's Depth of Knowledge (DOK) model, the framework encompasses four cognitive dimensions, five proficiency levels, and twenty task categories, providing a comprehensive assessment of model performance in terms of knowledge acquisition, syntactic generation, semantic alignment, execution accuracy, and robustness. In parallel, we developed GeoSQL-Bench, a benchmark dataset comprising 14178 questions that span three task types, 340 PostGIS functions, and 82 domain-specific databases. Leveraging this framework, we systematically evaluated 24 representative models across six categories, applying entropy-weighting and statistical analyses to reveal differences in performance, error distributions, and resource consumption patterns. Furthermore, we established a public GeoSQL-Eval leaderboard that enables global research teams to conduct ongoing testing and comparison. These contributions not only extend the boundaries of NL2SQL applications but also provide a standardized, interpretable, and scalable framework for evaluating LLM performance in spatial database contexts, offering valuable insights for model optimization and applications in geographic information science, urban studies, and spatial analysis.

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