Spatial sampling of population based on multi-source information and deep learning feature extraction

doi:10.13474/j.cnki.11-2246.2021.0238

Abstract

Abstract: Population sampling survey is a means to estimate the population of a region through population samples. Due to the spatial difference of population distribution, the traditional sampling survey theory is difficult to meet the growing demand of spatial sampling. The research on reasonable and efficient population spatial sampling survey method is of great significance to population statistics, human activities and urban problems. This paper proposes a population spatial sampling method based on multi-source information and deep learning feature extraction. Firstly, we use quadtree segmentation for stratified sampling with the help of impervious surface information, initially select the survey samples that may have population distribution.Secondly, we estimate building density of sample by convolution neural network that is a common model of deep learning to assist in the final sample selection and survey scheme formulation. The results show that this method can effectively screen the sampling areas closely related to population distribution, eliminate a large number of useless samples, improve the efficiency of population survey and save a lot of survey costs.

Key words: multi-source information, population sampling, quadtree, deep learning, feature extraction

CLC Number:

P208

CHUN Jin, ZHANG Xinchang, GUO Haijing, ZHANG Jianguo, JIN Shicheng. Spatial sampling of population based on multi-source information and deep learning feature extraction[J]. Bulletin of Surveying and Mapping, 2021, 0(8): 42-47.

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