摘要
Poverty has always been one of the topics concerned by governments and researchers all over the world, especially in developing countries. Remote sensing image is widely used in poverty estimation because of its large area observation, timeliness and periodicity. In this study, we explore the applicability of convolution neural network (CNN) combined with remote sensing image in regional poverty estimation. In the 2016 economic indicators estimation of Guizhou Province, China, the Pearson coefficient of per capita GDP (PCGDP) reached 0.76, which means that the image features extracted by CNN can explain the change of PCGDP of county level economic indicators up to 76%. Compared with other methods, our method still has high precision. Based on these results, we found that convolutional neural network combined with remote sensing image can be used in regional poverty estimation.
Poverty has always been one of the topics concerned by governments and researchers all over the world, especially in developing countries. Remote sensing image is widely used in poverty estimation because of its large area observation, timeliness and periodicity. In this study, we explore the applicability of convolution neural network (CNN) combined with remote sensing image in regional poverty estimation. In the 2016 economic indicators estimation of Guizhou Province, China, the Pearson coefficient of per capita GDP (PCGDP) reached 0.76, which means that the image features extracted by CNN can explain the change of PCGDP of county level economic indicators up to 76%. Compared with other methods, our method still has high precision. Based on these results, we found that convolutional neural network combined with remote sensing image can be used in regional poverty estimation.
