用智能体框架整合多模态遥感数据，实现复杂地球观测推理。
Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents
- 构建基于MCP的工具生态，融合可见光与光谱数据进行多步推理。
- 在248项任务上验证，支持跨模态定量时空分析，提升推理准确性。
- 适合遥感、气候建模等科研人员，推动地球观测智能化升级。
地球观测对理解地球系统动态至关重要。尽管近期多模态大模型（MLLMs）推动了地球观测研究，但其仍难以处理需多步推理和领域专用工具的复杂任务。现有基于智能体的方法仍处于初级阶段，局限于可见光感知、浅层推理且缺乏系统评估协议。为此，我们提出Earth-Agent，首个将可见光与光谱地球观测数据统一于MCP驱动工具生态系统中的智能体框架，实现超越预训练MLLM的跨模态、多步、定量时空推理。Earth-Agent通过动态调用多模态专家工具与模型，支持地质物理参数反演与定量时空分析等复杂科学任务。为支持全面评估，我们进一步构建Earth-Bench基准，包含248个专家标注任务、13,729张图像，覆盖光谱、产品与可见光模态，并配备双层级评估协议，衡量推理轨迹与最终结果。我们通过更换不同LLM主干、对比通用智能体框架及传统MLLMs在遥感基准上的实验，验证了Earth-Agent的有效性与潜力。该框架为地球观测分析树立新范式，推动领域迈向基于科学依据的下一代大模型应用。更多信息见https://github.com/opendatalab/Earth-Agent。
原文摘要 · Abstract (English)
Earth observation (EO) is essential for understanding the evolving states of the Earth system. Although recent MLLMs have advanced EO research, they still lack the capability to tackle complex tasks that require multi-step reasoning and the use of domain-specific tools. Agent-based methods offer a promising direction, but current attempts remain in their infancy, confined to RGB perception, shallow reasoning, and lacking systematic evaluation protocols. To overcome these limitations, we introduce Earth-Agent, the first agentic framework that unifies RGB and spectral EO data within an MCP-based tool ecosystem, enabling cross-modal, multi-step, and quantitative spatiotemporal reasoning beyond pretrained MLLMs. Earth-Agent supports complex scientific tasks such as geophysical parameter retrieval and quantitative spatiotemporal analysis by dynamically invoking expert tools and models across modalities. To support comprehensive evaluation, we further propose Earth-Bench, a benchmark of 248 expert-curated tasks with 13,729 images, spanning spectrum, products and RGB modalities, and equipped with a dual-level evaluation protocol that assesses both reasoning trajectories and final outcomes. We conduct comprehensive experiments varying different LLM backbones, comparisons with general agent frameworks, and comparisons with MLLMs on remote sensing benchmarks, demonstrating both the effectiveness and potential of Earth-Agent. Earth-Agent establishes a new paradigm for EO analysis, moving the field toward scientifically grounded, next-generation applications of LLMs in Earth observation. More information about Earth-Agent can be found at https://github.com/opendatalab/Earth-Agent
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