IC-EO: Interpretable Code-based assistant for Earth Observation

• URL: http://arxiv.org/abs/2602.00117v1
• Date: Tue, 27 Jan 2026 10:49:29 GMT
• Title: IC-EO: Interpretable Code-based assistant for Earth Observation
• Abstract summary: Earth Observation (EO) analysis remains difficult to perform for the laymen.<n>Many systems return black-box predictions that are difficult to audit or reproduce.<n>This study proposes a conversational, code-language agent that transforms natural-language queries into executable Python queries.
• Score: 5.490577355567582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 specific use cases. In this work, we select two use-cases of interest: land-composition mapping and post-wildfire damage assessment. The proposed agent outperforms general-purpose LLM/VLM baselines (GPT-4o, LLaVA), achieving 64.2% vs. 51.7% accuracy on land-composition and 50% vs. 0% on post-wildfire analysis, while producing results that are transparent and easy to interpret. By outputting verifiable code, the approach turns EO analysis into a transparent, reproducible process.

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