Toward Open Earth Science as Fast and Accessible as Natural Language

2025-05-21

AI Summary

This study addresses the challenge of natural language (NL)-driven Earth observation (EO) data analysis by introducing the first open-source, NL-driven analytical framework tailored for Earth science. Methodologically, it leverages open large language models (LLMs), integrating model scaling, prompt engineering, and inference-time scaling optimizations to construct a maintainable software stack and release a standardized benchmark dataset. Key contributions include: (1) establishing a multi-dimensional evaluation framework for NL-to-EO analysis; (2) achieving near-100% accuracy on 10 of 11 core EO analysis tasks; and (3) significantly reducing token consumption and end-to-end latency—demonstrating a cost-effective, low-latency, and sustainably evolvable technical pathway. The framework enables high-accuracy, low-latency, and fully reproducible open scientific practices, thereby advancing transparent, publicly beneficial Earth science research.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Evaluation and Analysis

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Is natural-language-driven earth observation data analysis now feasible with the assistance of Large Language Models (LLMs)? For open science in service of public interest, feasibility requires reliably high accuracy, interactive latencies, low (sustainable) costs, open LLMs, and openly maintainable software -- hence, the challenge. What are the techniques and programming system requirements necessary for satisfying these constraints, and what is the corresponding development and maintenance burden in practice? This study lays the groundwork for exploring these questions, introducing an impactful earth science use-case, and providing a software framework with evaluation data and metrics, along with initial results from employing model scaling, prompt-optimization, and inference-time scaling optimization techniques. While we attain high accuracy (near 100%) across 10 of 11 metrics, the analysis further considers cost (token-spend), latency, and maintainability across this space of techniques. Finally, we enumerate opportunities for further research, general programming and evaluation framework development, and ongoing work for a comprehensive, deployable solution. This is a call for collaboration and contribution.

Problem

• Feasibility of natural-language-driven earth observation using LLMs
• Techniques and system requirements for reliable, low-cost open science
• Balancing accuracy, latency, and maintainability in earth science analysis

Innovation

• Uses LLMs for earth observation data analysis
• Implements model and prompt-optimization techniques
• Develops open software framework with evaluation metrics