【摘要】 通过智能化提升制图技术，让制图系统能全自动地完成地图设计与制作，一直是地图学界追求的目标，也一直是国际地图制图协会的前沿研究方向。从20世纪80年代开始，人工智能技术在地图学领域开始应用，逐步解决了部分工序的自动化问题，提高了地图制图的生产效率。然而，地图设计等关键环节的自动化水平仍然极低，无法满足信息时代的“定制化”“泛在化”制图需求。可喜的是，2023年以来，以GPT-4和Gemini等大语言模型(简称“大模型”)为代表的人工智能技术取得了突破，达到了“准通用人工智能”,表现出令人惊叹的语言理解力、推理能力和表达能力。基于此，本文探讨利用大模型来提升地图制图系统的智能水平，旨在建立新一代智能化制图理论与方法体系。首先，分析现有数字制图系统的瓶颈问题，指出建立新一代智能化制图技术的必要性；其次，分析大模型的性质与能力，论证建立新一代智能化制图技术的充分性；然后，进一步分析它们相结合的可能与方式，提出一个大模型时代的智能制图模式，并根据其根本性质与表征，将之称为情境化地图表达；最后，讨论情境化地图表达的关键技术问题，即自主觉知用图情境、自主设计制作地图及随境自主人机交互。更多还原

【Abstract】 Making mapping system automatically conducting map design and production through intelligent techniques has always been the goal pursued by the cartographic community and the frontier research direction of the International Cartographic Association. Since the 1980s, artificial intelligence has been applied in cartography, gradually solving the automation problems of some processes and improving the production efficiency of map making. However, the level of automation in key steps such as map design is still extremely low, which cannot meet the “customized” and “ubiquitous” mapping demand in the information age. Fortunately, since 2023, artificial intelligence technology represented by large language models such as GPT-4 and Gemini has made breakthroughs and achieved “quasi-general artificial intelligence”, which shows strong language comprehension, reasoning and expression ability. This paper explores the use of large models to improve the intelligence level of map making systems, aiming to establish a new generation of intelligent mapping theory and method system. This paper first analyzes the bottleneck problems of the existing digital mapping system and points out the necessity of establishing a new generation of intelligent mapping technology; then it analyzes the nature and capabilities of large models and demonstrates the sufficiency of establishing such a new generation; then it further analyzes the possibility and methods of combining them, proposes an intelligent mapping framework in the era of large models(e.g. situatedness map representation); finally, it discusses the key technical issues of situatedness map representation: “autonomous consciousness of mapping context”, “autonomous design and production of maps” and “autonomous human-computer interaction in situatedness ”.更多还原