Study on man-machine collaborative intelligent extraction for natural resource features

doi:10.11947/j.AGCS.2021.20210102

Abstract

Abstract: Carrying out an integrated survey, monitoring and evaluation of natural resources, accurately understanding the status and changes of various natural resources in China, is the scientific basis for territorial and spatial plans, and gradually realizing the overall protection, restoration, and comprehensive management of landscapes (including mountains, forests, fields, lakes and grasses), ensuring national ecological security. At present, the extraction of natural resource features based on remote sensing images mainly relies on visual interpretation via man-machine interaction and field spot verification. It needs high labor intensity, and production efficiency is low. The results are also highly affected by man-induced factor, which can no longer adapt to the requirements for integrated investigation and monitoring of all features of natural resources. This paper conforms to the emerging direction of the research development with artificial intelligence collaboration. Firstly, this paper reviews the main research progress of deep learning technology and its application in the field of remote sensing image intelligent extraction systematically, and analyzes its limitations, then it reviews the main research status of man-machine collaboration. Afterward, Starting from the characteristics of natural resource features, presents a technical framework of "intelligent background computing+intelligent engine+man-machine interface" for man-machine collaborative intelligent extraction. The key technologies that need to be overcome are described. At last, the idea of creating cloud platform for feature extraction are discussed. This paper aims to provide a new AI method for intelligent extraction and improve the automation and intelligence level of natural resource feature extraction.

Key words: natural resources survey and monitoring, natural resources features, man-machine cooperation, artificial intelligence, intelligent extraction

CLC Number:

P208

ZHANG Jixian, LI Haitao, GU Haiyan, ZHANG He, YANG Yi, TAN Xiangrui, LI Miao, SHEN Jing. Study on man-machine collaborative intelligent extraction for natural resource features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1023-1032.

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