Gallo, K.P., Owen, T.W., Gallo, K.P. and Owen, T.W. (1999) Satellite-Based Adjustments for the Urban Heat Island Temperature Bias. Journal of Applied Meteorology, 38, 806-813.
https://doi.org/10.1175/1520-0450(1999)038<0806:SBAFTU>2.0.CO;2

has been cited by the following article:

• TITLE: Estimation of Poverty Based on Remote Sensing Image and Convolutional Neural Network

  AUTHORS: Peng Wu, Yumin Tan

  KEYWORDS: Poverty, Convolution Neural Network, Remote Sensing Image, Economic Indicators, Guizhou, PCGDP

  JOURNAL NAME: Advances in Remote Sensing, Vol.8 No.4, December 19, 2019

  ABSTRACT: 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.