IC-EO: Interpretable Code-based assistant for Earth Observation
Lamia Lahouel, Laurynas Lopata, Simon Gruening, Gabriele Meoni, Gaetan Petit, Sylvain Lobry

TL;DR
This paper introduces IC-EO, an interpretable, code-based conversational agent that transforms natural language queries into executable Python workflows for Earth Observation analysis, enhancing transparency and reproducibility.
Contribution
It presents a novel framework combining tool LLMs with a unified API to generate auditable code for EO tasks, outperforming baseline models in accuracy and interpretability.
Findings
Achieves 64.2% accuracy on land-composition mapping
Outperforms baselines like GPT-4o and LLaVA in EO tasks
Produces transparent, verifiable code for EO analysis
Abstract
Despite recent advances in computer vision, Earth Observation (EO) analysis remains difficult to perform for the laymen, requiring expert knowledge and technical capabilities. Furthermore, many systems return black-box predictions that are difficult to audit or reproduce. Leveraging recent advances in tool LLMs, this study proposes a conversational, code-generating agent that transforms natural-language queries into executable, auditable Python workflows. The agent operates over a unified easily extendable API for classification, segmentation, detection (oriented bounding boxes), spectral indices, and geospatial operators. With our proposed framework, it is possible to control the results at three levels: (i) tool-level performance on public EO benchmarks; (ii) at the agent-level to understand the capacity to generate valid, hallucination-free code; and (iii) at the task-level on…
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Taxonomy
TopicsGeographic Information Systems Studies · Remote-Sensing Image Classification · Advanced Neural Network Applications
