让科研人员用Python分析卫星数据时自动转成可扩展的Spark程序
GRAIL: AI translation for scientists application workflow on satellite data
- 将科学家写的Python地理空间代码转为Spark程序，无需学新框架
- 在真实工作流上验证，翻译后代码正确且能处理大规模数据
- 用分步管道设计支持精准修复，避免重写整个程序
领域科学家越来越多地使用Python脚本分析卫星影像，但难以扩展到大规模数据。本文展示GRAIL，一种代理式翻译系统，可在不需科学家学习新框架的情况下，将Python地理空间工作流转换为可执行的Spark程序。GRAIL不依赖微调专用大模型，而是通过结构化文档、API别名函数和面向修复的错误日志，使适用于卫星数据的Scala库RDPro具备LLM可用性。翻译过程采用LangGraph流水线，将代码生成分解为具有明确输入输出的独立阶段，实现定向修复而无需重新生成完整程序。我们在真实地理空间工作流上展示了GRAIL的正确性和可扩展性。
原文摘要 · Abstract (English)
Domain scientists increasingly develop Python scripts to analyze satellite imagery but they lack scalability to large-scale data. This paper demonstrates GRAIL, an agentic translation system that converts Python geospatial workflows into executable Spark-based programs without requiring scientists to learn a new framework. Rather than fine-tuning a specialized LLM model, GRAIL adapts RDPro, a Scala library for satellite data analysis, to make it LLM-ready using structured documentation, API alias functions, and repair-oriented error logs. Translation is structured as a LangGraph pipeline that decomposes code generation into explicit sections with guided inputs and outputs, enabling targeted repair without regenerating the full program. We demonstrate GRAIL on real-world geospatial workflows and showcase the correctness and scalability of the translated code.
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