Peilin Feng1*,
Zhutao Lv1,2*,
Junyan Ye1,2,
Xiaolei Wang2,
Xinjie Huo2,
Jinhua Yu2,
Wanghan Xu1,
Wenlong Zhang1,
Lei Bai1,
Conghui He1,
Weijia Li1,2†
1Shanghai Artificial Intelligence Laboratory, 2Sun Yat-sen University
- [2025.10.27]: 🎉 We are excited to announce that Synced Review has reported on our article. You can find more details here.
- [2025.10.17]: 🤗 We are excited to release the Earth-Bench dataset. Check out on huggingface.
- [2025.9.27]: 🔥 We have released Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents. Check out the paper. We present Earth-Agent and Earth-Bench.
We introduce Earth-Agent, an EO agent framework cast as a ReAct-style Partially Observable Markov Decision Process (POMDP). The LLM serves as the policy, iterating a loop of tool calling, memory update, deliberation, and action to solve tasks conditioned on goal and interaction history. Besides, Earth-Agent integrates 104 specialized tools across five functional kits, i.e. Index, Inversion, Perception, Analysis, and Statistics, spanning perceptual and spectral analysis. To evaluate both outcomes and reasoning, we adopt a dual-level protocol: end-to-end assessment of final Accuracy and trajectory Efficiency, and step-by-step checks of Tool-Any-Order, Tool-In-Order, Tool-Exact-Match, and Parameter Accuracy to characterize the completeness and fidelity of reasoning trajectories.
- We propose Earth-Agent, a revolutionary paradigm shift from traditional MLLMs to agentic EO analysis, unifying RGB and spectral EO data within an MCP-based tool ecosystem
- In order to comprehensivly evaluate Earth-Agent, we propose Earth-Bench, which covers Spectrum, Products and RGB modality for scientific workflows requring tool interaction,
- Earth-Agent substantially outperforms general agents and surpasses remote sensing MLLMs on remote sensing benchmarks, demonstrating both effectiveness and potential for advancing EO research
Download the benchmark dataset from Hugging Face: