Autonomous situatedness map representation: a theoretical discussion on intelligent cartography in the era of large models

doi:10.11947/j. AGCS.2024.20240222.

Abstract

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 ”.

Key words: intelligent surveying and mapping, cartography, situatedness map representation, large model

CLC Number:

P208

Zhilin LI, Zhu XU, Li SHEN, Jingzhong LI, Tian LAN, Jicheng WANG, Tingting ZHAO, Tinghua AI, Peng TI, Wanzeng LIU, Jun CHEN. Autonomous situatedness map representation: a theoretical discussion on intelligent cartography in the era of large models[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(11): 2043-2052.

Figures/Tables 4

References 61

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