EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents

The inherent interactivity and multimodal nature of Earth Observation (EO) analysis have long presented a significant challenge for AI development, with most existing benchmarks simplifying complex processes into fixed-input, single-turn tasks. EO-Gym emerges as a critical advancement, providing a controlled, executable framework specifically designed for multimodal, tool-using EO agents. By formulating EO analysis as a Gymnasium-style local geospatial workspace, it accurately reflects the dynamic, evidence-gathering nature of real-world EO tasks, where resolving uncertainty often requires expanding regions of interest, retrieving historical data, and switching between various sensor types.

EO-Gym's robust infrastructure is backed by an extensive dataset of over 660,000 multimodal files, meticulously indexed by location, time, and sensor type, including optical and Synthetic Aperture Radar imagery. This rich data environment is complemented by 35 EO-specialized tools, categorized across six task families, enabling agents to perform complex operations. Furthermore, the accompanying EO-Gym-Data benchmark, comprising 9,078 trajectories and 34,604 reasoning steps, provides a rigorous evaluation platform. Initial assessments of 10 open and closed Vision-Language Models (VLMs) revealed that even strong general-purpose models struggle with interactive EO reasoning, particularly concerning temporal and cross-modal workflows. However, the fine-tuned Qwen3-VL-4B-Instruct model, designated EO-Gym-4B, demonstrated a significant improvement in overall Pass@3 from 0.49 to 0.74, establishing a strong reference baseline.

The implications of EO-Gym are transformative for the field of AI agents and Earth Observation. By operationalizing EO as an evidence-gathering problem that necessitates planning across geospatial, temporal, and sensing modalities, it provides a reproducible environment for developing and evaluating agents capable of sophisticated, interactive analysis. This framework will accelerate research into more capable and autonomous EO agents, leading to breakthroughs in critical applications such as climate change monitoring, disaster prediction and response, resource management, and urban development. The ability to train and test agents in a truly interactive, multimodal setting is a crucial step towards unlocking the full potential of AI in understanding and managing our planet.

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