OpenEarthAgent：一个用于工具增强地理空间代理的统一框架
OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents
AI总结：
本文提出OpenEarthAgent框架，通过整合卫星影像、自然语言查询和结构化推理轨迹，实现多模态地理空间推理，提升遥感任务的执行能力与空间逻辑一致性。
摘要：
近期多模态推理的进展使能够解释图像、连接语言并执行结构化分析任务的代理成为可能。将这些能力扩展到遥感仍然具有挑战性，因为模型必须在空间尺度、地理结构和多光谱指数上进行推理，同时保持连贯的多步逻辑。为了解决这一差距，我们引入OpenEarthAgent，一个统一的框架，用于工具增强的地理空间推理，训练于卫星影像、自然语言查询和结构化推理轨迹。除了作为基准之外，OpenEarthAgent建立了一个围绕统一可执行工具注册表和轨迹基政策学习的统一代理架构。该框架在一致的可调用模式下标准化了异构视觉、光谱、GIS和地理参考栅格操作，使模块化编排和确定性执行成为可能。通过在结构化推理轨迹上进行监督微调，并通过确定性回放验证确保可执行性和空间正确性进行训练。配套的数据集包含14,538个训练实例和1,169个评估实例，超过107,000个推理步骤，涵盖城市、环境、灾害和基础设施领域，并结合GIS操作以及如NDVI、NBR和NDBI等指数分析。基于显式的推理轨迹，学习的代理在多样化的遥感场景中表现出结构化的推理、稳定的空问理解以及可解释的工具驱动行为。我们报告了相对于强基线的一致改进，并在最近的开源和闭源模型中表现出竞争力。我们的代码和训练模型将公开发布。
英文摘要：
Recent progress in multimodal reasoning has enabled agents that interpret imagery, connect it with language, and execute structured analytical tasks. Extending these capabilities to remote sensing remains challenging, as models must reason over spatial scale, geographic structures, and multispectral indices while maintaining coherent multi-step logic. To address this gap, we introduce \textit{OpenEarthAgent}, a unified framework for tool-augmented geospatial reasoning trained on satellite imagery, natural-language queries, and structured reasoning traces. Beyond serving as a benchmark, OpenEarthAgent establishes a cohesive agentic architecture built around a unified executable tool registry and trajectory-based policy learning. The framework standardizes heterogeneous visual, spectral, GIS, and georeferenced raster operations under a consistent callable schema, enabling modular orchestration and deterministic execution. Training is performed via supervised fine-tuning on structured reasoning trajectories with deterministic replay validation to ensure executability and spatial correctness. The accompanying corpus comprises 14,538 training and 1,169 evaluation instances with over 107K reasoning steps, spanning urban, environmental, disaster, and infrastructure domains and incorporating GIS operations alongside index analyses such as NDVI, NBR, and NDBI. Grounded in explicit reasoning traces, the learned agent demonstrates structured reasoning, stable spatial understanding, and interpretable tool-driven behaviour across diverse EO scenarios. We report consistent improvements over a strong baseline and competitive performance against recent open and closed-source models. Our code, data and trained models are publicly available: https://github.com/mbzuai-oryx/OpenEarthAgent
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。
