Earth-Agent: 解锁地球观测的全貌与潜力
Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents
AI总结：
Earth-Agent是一种结合RGB和光谱数据的代理框架，通过多模态工具生态系统实现跨模态、多步骤推理，提升地球观测分析的科学性和应用潜力。
摘要：
地球观测（EO）对于理解地球系统的演变状态至关重要。尽管最近的多模态大语言模型（MLLMs）已推动了EO研究，但它们仍缺乏处理需要多步骤推理和使用领域特定工具的复杂任务的能力。基于代理的方法提供了一个有前途的方向，但当前的尝试仍处于初级阶段，局限于RGB感知、浅层推理和缺乏系统评估协议。为了克服这些限制，我们引入了Earth-Agent，这是首个将RGB和光谱EO数据统一在MCP工具生态系统中的代理框架，使跨模态、多步骤和定量时空推理成为可能，超越了预训练的MLLMs。Earth-Agent通过动态调用跨模态的专家工具和模型，支持复杂的科学任务，如地物理参数检索和定量时空分析。为了支持全面评估，我们进一步提出了Earth-Bench，一个包含248个专家精心挑选的任务和13729张图像的基准，涵盖光谱、产品和RGB模态，并配备了双级评估协议，评估推理轨迹和最终结果。我们进行了全面的实验，测试了不同LLM后端、与通用代理框架的比较以及与遥感基准的MLLMs比较，证明了Earth-Agent的有效性和潜力。Earth-Agent建立了一个新的EO分析范式，将该领域推向基于科学的、下一代LLMs在地球观测中的应用。更多关于Earth-Agent的信息可以在https://github.com/opendatalab/Earth-Agent上找到。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
