An automated, execution-based benchmark of 1,325 Google Earth Engine unit tests shows 18 LLMs scoring between 31.40% and 71.55% pass@1, with parameter-knowledge errors the dominant failure mode.
Learning to code or coding to learn? A systematic review
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AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models
An automated, execution-based benchmark of 1,325 Google Earth Engine unit tests shows 18 LLMs scoring between 31.40% and 71.55% pass@1, with parameter-knowledge errors the dominant failure mode.