GeoVR: Structured Execution Rewards for Reinforcement Learning in Geospatial Code Generation

Large language models are increasingly used to generate geospatial analysis code, yet executable programs can still be analytically wrong: an incorrect data source, temporal range, coordinate reference system, or workflow often fails silently. Existing reinforcement learning with verifiable rewards (RLVR) for code relies on unit tests or execution outcomes, which cannot detect such errors. We introduce GeoVR, an RLVR framework for geospatial code generation built on Google Earth Engine (GEE). Its core reward, Hierarchical Schema-grounded Verifiable Reward (HSVR), extracts type-aware signatures from GEE execution artifacts (object type, bands, CRS, spatiotemporal extent, and verifiable statistics) and checks required analytical operations in the program AST, yielding four deterministic components: Execution, Type, Schema, and Method. A hierarchical gate activates Schema and Method rewards only when the primary result passes task-level verification with the correct output type, preventing incorrect programs from accumulating reward through partially satisfied constraints. GeoVR is optimized with GRPO and leverages structured execution errors to repair failed rollouts. On AutoGEEval++, GeoVR achieves pass@1 of 90.22%, 88.76%, and 67.82% on Atomic, Combined, and Theme tasks, outperforming CodeGEEnius by up to 6.38 points and a controlled Binary-RLVR baseline by 3.73–10.35 points. These results demonstrate that execution artifacts themselves can serve as effective verification feedback for reinforcement learning, enabling models to move beyond generating merely "executable programs" toward generating "task-correct programs that satisfy domain constraints." Data and code are available at: https://figshare.com/s/03091d54357e8a152e7a