GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots
<|reference_start|>GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots: Geospatial Copilots unlock unprecedented potential for performing Earth Observation (EO) applications through natural language instructions. However, existing agents rely on overly simplified single tasks and template-based prompts, creating a disconnect with real-world scenarios. In this work, we present GeoLLM-Engine, an environment for tool-augmented agents with intricate tasks routinely executed by analysts on remote sensing platforms. We enrich our environment with geospatial API tools, dynamic maps/UIs, and external multimodal knowledge bases to properly gauge an agent's proficiency in interpreting realistic high-level natural language commands and its functional correctness in task completions. By alleviating overheads typically associated with human-in-the-loop benchmark curation, we harness our massively parallel engine across 100 GPT-4-Turbo nodes, scaling to over half a million diverse multi-tool tasks and across 1.1 million satellite images. By moving beyond traditional single-task image-caption paradigms, we investigate state-of-the-art agents and prompting techniques against long-horizon prompts.<|reference_end|>
@article{singh2024geollm-engine:,
    title={GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots},
    author={Simranjit Singh, Michael Fore, Dimitrios Stamoulis},
    journal={arXiv preprint arXiv:2404.15500},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15500},
    primaryClass={cs.AI cs.CL cs.LG}
}