EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents
Earth Observation (EO) analysis is inherently interactive: resolving uncertainty often requires expanding the region of interest, retrieving historical observations, and switching across sensors such as optical and Synthetic Aperture Radar. However, most EO benchmarks collapse this process into fixed-input, single-turn tasks. To address this gap, we present EO-Gym, a controlled executable framework for multimodal, tool-using EO agents that formulates EO analysis as a Gymnasium-style local geospatial workspace backed by more than 660k multimodal files indexed by location, time, and sensor type, with 35 EO-specialized tools spanning six task families. Built on this environment, we construct...
| Authors | Sai Ma, Zhuang Li, Sichao Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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