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

arXiv: https://arxiv.org/abs/2602.00117 · HTML: https://mlsanity.com/paper/2602.00117/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://mlsanity.com/paper/2303.08128/) (2023)
- [A Survey on Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2402.00253/) (2024)
- [GeoChat: Grounded Large Vision-Language Model for Remote Sensing](https://mlsanity.com/paper/2311.15826/) (2023)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [RSGPT: A Remote Sensing Vision Language Model and Benchmark](https://mlsanity.com/paper/2307.15266/) (2023)
- [RSVQA: Visual Question Answering for Remote Sensing Data](https://mlsanity.com/paper/2003.07333/) (2020)
- [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://mlsanity.com/paper/2412.02732/) (2024)
- [GEO-Bench: Toward Foundation Models for Earth Monitoring](https://mlsanity.com/paper/2306.03831/) (2023)
- [SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model](https://mlsanity.com/paper/2401.09712/) (2024)
- [Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation](https://mlsanity.com/paper/2403.15356/) (2024)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis](https://mlsanity.com/paper/2403.19646/) (2024)
- [RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent](https://mlsanity.com/paper/2406.07089/) (2024)
- [Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents](https://mlsanity.com/paper/2509.23141/) (2025)
- [Multi-Agent Geospatial Copilots for Remote Sensing Workflows](https://mlsanity.com/paper/2501.16254/) (2025)
- [Towards LLM Agents for Earth Observation](https://mlsanity.com/paper/2504.12110/) (2025)
- [FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery](https://mlsanity.com/paper/2305.14467/) (2023)
- [Analyzing Modular Approaches for Visual Question Decomposition](https://mlsanity.com/paper/2311.06411/) (2023)
- [Visual Question Answering on Multiple Remote Sensing Image Modalities](https://mlsanity.com/paper/2505.15401/) (2025)
- [RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation](https://mlsanity.com/paper/2512.05025/) (2025)
- [SAR Strikes Back: A New Hope for RSVQA](https://mlsanity.com/paper/2501.08131/) (2025)
- [Checkmate: interpretable and explainable RSVQA is the endgame](https://mlsanity.com/paper/2508.13086/) (2025)

## Built on by (most cited first, 2 of 2)

- [GroundSet: A Cadastral-Grounded Dataset for Spatial Understanding with Vector Data](https://mlsanity.com/paper/2603.14609/) (2026)
- [AI4Fire: Evaluating Large Language Models on Wildfire Tasks](https://mlsanity.com/paper/2610.10946/) (2026)

## Abstract (arXiv)

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.