Hybrid computational paradigm and methods for intelligentized surveying and mapping

doi:10.11947/j.AGCS.2024.20240131

Abstract

Abstract:

The products from traditional digital surveying and mapping play an increasingly important role as the spatiotemporal foundation and key production element in the field of digital economy, digital governance, and digital life. However, their level of detail, update cycle, and service mode struggle to meet the high-quality development demands of the new digital intelligence era. Thus, there is an urgent need to transition from digital to intelligentized surveying and mapping, constructing new spatiotemporal infrastructures to comprehensively enhance the supply of high-quality spatiotemporal information resources, high-level spatiotemporal analysis capabilities, and high-standard spatiotemporal knowledge services. Starting from the analysis of the necessity of combining natural intelligence and artificial intelligence in surveying and mapping, this paper firstly discusses the basic overview and connotation of intelligent computing for surveying and mapping. Then we propose a new paradigm of KDAS hybrid intelligent computation for intelligent mapping, and sort out the basic tasks of its construction. Finally, this paper systematically elaborates the research method of hybrid computation for the key tasks of intelligentized surveying and mapping including perception, cognition, expression and service, as well as pointing out the basic research directions for the construction of the intelligent knowledge system and the industrial upgradation empowered by hybrid computing.

Key words: intelligentized surveying and mapping, spatiotemporal hybrid intelligence, KDAS computing paradigm of hybrid intelligence, methodology of hybrid computation

CLC Number:

Jun CHEN, Tinghua AI, Li YAN, Wanzeng LIU, Zhilin LI, Qiang ZHU, Jingxiang GAO, Hong XIE, Hao WU, Jun ZHANG. Hybrid computational paradigm and methods for intelligentized surveying and mapping[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 985-998.

Figures/Tables 3

References 86

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