Journal of Geodesy and Geoinformation Science ›› 2022, Vol. 5 ›› Issue (2): 148-160.doi: 10.11947/j.JGGS.2022.0213

• Literature Review • Previous Articles    

From Digitalized to Intelligentized Surveying and Mapping: Fundamental Issues and Research Agenda

Jun CHEN1(),Zhilin LI2,3,Songnian LI4,Wanzeng LIU1(),Hao WU1,Li YAN5   

  1. 1. National Geomatics Center of China, Beijing, 100830, China
    2. Faculty of Geosciences & State-Province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China
    3. Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China
    4. Department of Civil Engineering, Toronto Metropolitan University, Toronto M5B 1E5, Canada
    5. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
  • Received:2021-02-15 Accepted:2022-03-19 Online:2022-06-20 Published:2022-07-22
  • Contact: Wanzeng LIU;
  • About author:Jun CHEN (1956—), male, professor, academician of Chinese Academy of Engineering, majors in data modeling, updating and service of geospatial information. E-mail:
  • Supported by:
    The Key Program of the National Natural Science Foundation of China(41930650);The Strategic Consulting Project of Chinese Academy of Engineering(2019-ZD-16)


Nowadays Surveying and Mapping (S&M) production and services are facing some serious challenges such as real-timization of data acquisition, automation of information processing, and intellectualization of service applications. The main reason is that current digitalized S&M technologies, which involve complex algorithms and models as the core, are incapable of completely describing and representing the diverse, multi-dimensional and dynamic real world, as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models. In order to address these challenges, it is necessary to explore the use of natural intelligence in S&M, and to develop intelligentized S&M technologies, which are knowledge-guided and algorithm-based. This paper first discusses the basic concepts and ideas of intelligentized S&M, and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M, the construction and realization of hybrid intelligent computing paradigm, and the mechanism and path of empowering production. Further research directions are then proposed in the four areas, including knowledge systems, technologies and methodologies, application systems, and instruments and equipments of intelligentized S&M. Finally, some institutional issues related to promoting scientific research and engineering applications in this area are discussed.

Key words: Surveying and Mapping; intelligentization; natural intelligence; hybrid intelligent computing