Reflection on the application of deep learning method in “multiple-investigation-coordination” in Shenzhen

doi:10.13474/j.cnki.11-2246.2021.0026

Abstract

Abstract: Multiple-investigation-coordination is an effective method to achieve data fusion from varies kinds of investigation activities which solves the problem of heterogeneity and conflict of data source. The foundation of the work requires not only unified investigation and estimation system but also synchronously pushing forward the basic investigation and special investigation based on the local circumstances. As a result, to make sure the correctness of acquisition of investigate unit of special investigation, the first priority is to keep the validity of the basic unit division. Moreover, the correctness of classification in the both investigating activities should be guaranteed. In the paper, we consider the application prospect of deep learning method of image classification and object recognition in the “Multiple-investigation-coordination” works in Shenzhen in order to reduce the human labor compared with traditional outdoor investigating tasks and analyze how to improve the accuracy of the classification result. With the continuous improvement of deep learning method, the effectiveness of such method applied in the natural resource investigation activities will enhance constantly.

Key words: multiple-investigation-coordination, basic investigation, special investigation, deep learning, image classification, object recognition

CLC Number:

P208

SUN Wei, LI Sheng, JIANG Yi, KE Shuisong, XIA Anke. Reflection on the application of deep learning method in “multiple-investigation-coordination” in Shenzhen[J]. Bulletin of Surveying and Mapping, 2021, 0(1): 138-141.

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