为地理信息系统助手设计9种核心智能能力，提升人类协作效率。
GeoAI Agency Primitives
- 提出9种地理智能体基础能力，支持人机协同工作
- 构建基准测试，量化人类在地理任务中的生产力提升
- 聚焦矢量图层与地图制作，填补模型与实际工作间的空白
我们介绍了关于地理信息智能体基础能力的持续研究——这些核心功能将基础模型与地理信息系统（GIS）从业者实际采用的以成果为中心、人机协同的工作流相连接。尽管卫星图像描述、视觉问答和可提示分割等技术取得进展，但这些能力尚未转化为从业者在生成矢量图层、栅格地图和制图产品时的生产力提升。问题不仅在于模型能力，更在于缺乏支持迭代协作的智能代理层。我们提出了一个包含9种基础能力的词汇表，包括导航、感知、地理参考记忆和双模型建模等，并建立了一个衡量人类生产力的基准。目标是打造一套可实现、可测试、可比较的地理智能代理能力体系。
原文摘要 · Abstract (English)
We present ongoing research on agency primitives for GeoAI assistants -- core capabilities that connect Foundation models to the artifact-centric, human-in-the-loop workflows where GIS practitioners actually work. Despite advances in satellite image captioning, visual question answering, and promptable segmentation, these capabilities have not translated into productivity gains for practitioners who spend most of their time producing vector layers, raster maps, and cartographic products. The gap is not model capability alone but the absence of an agency layer that supports iterative collaboration. We propose a vocabulary of $9$ primitives for such a layer -- including navigation, perception, geo-referenced memory, and dual modeling -- along with a benchmark that measures human productivity. Our goal is a vocabulary that makes agentic assistance in GIS implementable, testable, and comparable.
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