Leaderboard for Large Language Models on Geospatial Code Generation (AutoGEEval & AutoGEEval++)
| Rank | Model | Developer | AutoGEEval Pass@1 |
AutoGEEval Pass@3 |
AutoGEEval Pass@5 |
AutoGEEval++ Pass@1 |
AutoGEEval++ Pass@3 |
AutoGEEval++ Pass@5 |
Type |
|---|
Related Papers
Our research publications
AutoGEEval: A Multimodal and Automated Evaluation Framework for Geospatial Code Generation on GEE with Large Language Models
ISPRS International Journal of Geo-Information, July 2025
AutoGEEval++: A Multi-Level and Multi-Geospatial-Modality Automated Evaluation Framework for Large Language Models in Geospatial Code Generation on Google Earth Engine
arXiv preprint, June 2025
GeoGraphRAG: A graph-based retrieval-augmented generation approach for empowering large language models in automated geospatial modeling
International Journal of Applied Earth Observation and Geoinformation, August 2025
Chain-of-programming (CoP): empowering large language models for geospatial code generation task
International Journal of Digital Earth, May 2025
Design and application of a semantic-driven geospatial modeling knowledge graph based on large language models
Geo-spatial Information Science, May 2025
GEE-OPs: An operator knowledge base for geospatial code generation on the Google Earth Engine platform powered by large language models
Geo-spatial Information Science, May 2025
Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation with Large Language Models
Knowledge-Based Systems, April 2025
GeoCode-GPT: A large language model for geospatial code generation
International Journal of Applied Earth Observation and Geoinformation, March 2025
A GPT-enhanced framework on knowledge extraction and reuse for geographic analysis models in Google Earth Engine
International Journal of Digital Earth, September 2024