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.
References
[1]
Zhao, X., et al. (2019) Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh. Remote Sensing, 11, 375. https://doi.org/10.3390/rs11040375
[2]
Engstrom, R., Newhouse, D., Haldavanekar, V., Copenhaver, A. and Hersh, J. (2017) Evaluating the Relationship between Spatial and Spectral Features Derived from High Spatial Resolution Satellite Data and Urban Poverty in Colombo, Sri Lanka. 2017 Joint Urban Remote Sensing Event, Dubai, 6-8 March 2017, 8-11. https://doi.org/10.1109/JURSE.2017.7924590
[3]
Ferreira, F.H.G., et al. (2016) A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results. The Journal of Economic Inequality, 14, 141-172. https://doi.org/10.1007/s10888-016-9326-6
[4]
Sutton, P.C. and Costanza, R. (2002) Global Estimates of Market and Non-Market Values Derived from Nighttime Satellite Imagery, Land Cover, and Ecosystem Service Valuation. Ecological Economics, 41, 509-527. https://doi.org/10.1016/S0921-8009(02)00097-6
Cui, X., Lei, Y., Zhang, F., Zhang, X. and Wu, F. (2019) Mapping Spatiotemporal Variations of CO2 (Carbon Dioxide) Emissions Using Nighttime Light Data in Guangdong Province. Physics and Chemistry of the Earth, 110, 89-98. https://doi.org/10.1016/j.pce.2019.01.007
[7]
Ou, J., Liu, X., Li, X., Li, M. and Li, W. (2015) Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data. PLoS ONE, 10, e0138310. https://doi.org/10.1371/journal.pone.0138310
[8]
Marx, A. and Ziegler Rogers, M. (2017) Analysis of Panamanian DMSP/OLS Nightlights Corroborates Suspicions of Inaccurate Fiscal Data: A Natural Experiment Examining the Accuracy of GDP Data. Remote Sensing Applications: Society and Environment, 8, 99-104, . https://doi.org/10.1016/j.rsase.2017.08.005
[9]
Shi, K., et al. (2014) Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data. Remote Sensing, 6, 1705-1724. https://doi.org/10.3390/rs6021705
[10]
Zhao, N., Liu, Y., Cao, G., Samson, E.L. and Zhang, J. (2017) Forecasting China’s GDP at the Pixel Level Using Nighttime Lights Time Series and Population Images. GIScience & Remote Sensing, 54, 407-425. https://doi.org/10.1080/15481603.2016.1276705
[11]
Sutton, P., Roberts, D., Elvidge, C. and Baugh, K. (2001) Census from Heaven: An Estimate of the Global Human Population Using Night-Time Satellite Imagery. International Journal of Remote Sensing, 22, 3061-3076. https://doi.org/10.1080/01431160010007015
[12]
Li, C., Li, G., Zhu, Y., Ge, Y., Te Kung, H. and Wu, Y. (2017) A Likelihood-Based Spatial Statistical Transformation Model (LBSSTM) of Regional Economic Development Using DMSP/OLS Time-Series Nighttime Light Imagery. Spatial Statistics, 21, 421-439. https://doi.org/10.1016/j.spasta.2017.03.004
[13]
Wu, J., Wang, Z., Li, W. and Peng, J. (2013) Exploring Factors Affecting the Relationship between Light Consumption and GDP Based on DMSP/OLS Nighttime Satellite Imagery. Remote Sensing of Environment, 134, 111-119. https://doi.org/10.1016/j.rse.2013.03.001
[14]
Elvidge, C.D., Zhizhin, M., Hsu, F.C. and Baugh, K.E. (2013) VIIRS Nightfire: Satellite Pyrometry at Night. Remote Sensing, 5, 4423-4449. https://doi.org/10.3390/rs5094423
[15]
Yu, B., et al. (2014) Object-Based Spatial Cluster Analysis of Urban Landscape Pattern Using Nighttime Light Satellite Images: A Case Study of China. International Journal of Geographical Information Science, 28, 2328-2355. https://doi.org/10.1080/13658816.2014.922186
[16]
Elvidge, C.D., Zhizhin, M., Hsu, F.-C. and Baugh, K. (2013) What Is So Great about Nighttime VIIRS Data for the Detection and Characterization of Combustion Sources? Proceedings of the Asia-Pacific Advanced Network, 35, 33-48. https://doi.org/10.7125/apan.35.5
[17]
Elvidge, C.D., Baugh, K.E., Zhizhin, M. and Hsu, F.-C. (2013) Why VIIRS Data Are Superior to DMSP for Mapping Nighttime Lights. Proceedings of the Asia-Pacific Advanced Network, 35, 62-69. https://doi.org/10.7125/APAN.35.7
[18]
Noor, A.M., Alegana, V.A., Gething, P.W., Tatem, A.J. and Snow, R.W. (2008) Using Remotely Sensed Night-Time Light as a Proxy for Poverty in Africa. Population Health Metrics, 6, 5. https://doi.org/10.1186/1478-7954-6-5
[19]
Li, X., Xu, H., Chen, X. and Li, C. (2013) Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China. Remote Sensing, 5, 3057-3081. https://doi.org/10.3390/rs5063057
[20]
Yu, B., Shi, K., Hu, Y., Huang, C., Chen, Z. and Wu, J. (2015) Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 1217-1229. https://doi.org/10.1109/JSTARS.2015.2399416
[21]
Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B. and Ermon, S. (2016) Combining Satellite Imagery and Machine Learning to Predict Poverty. Science, 353, 790-794. https://doi.org/10.1126/science.aaf7894
[22]
Perez, A., Ganguli, S., Ermon, S., Azzari, G., Burke, M. and Lobell, D. (2019) Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty.
[23]
He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 27-30 June 2016, 770-778. https://doi.org/10.1109/CVPR.2016.90
[24]
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B. and Belongie, S. (2017) Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 21-26 July 2017, 936-944. https://doi.org/10.1109/CVPR.2017.106
[25]
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
[26]
Yuan, F. and Bauer, M.E. (2007) Comparison of Impervious Surface Area and Normalized Difference Vegetation Index as Indicators of Surface Urban Heat Island Effects in Landsat Imagery. Remote Sensing of Environment, 106, 375-386. https://doi.org/10.1016/j.rse.2006.09.003
[27]
Xu, H. (2006) Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27, 3025-3033. https://doi.org/10.1080/01431160600589179
[28]
Zha, Y., Gao, J. and Ni, S. (2003) Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing, 24, 583-594. https://doi.org/10.1080/01431160304987
[29]
Guizhou Provincial Bureau of Statistics. http://stjj.guizhou.gov.cn/tjsj_35719/sjcx_35720/gztjnj_40112/tjnj2018/
[30]
Xie, M., Jean, N., Burke, M., Lobell, D. and Ermon, S. (2015) Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Computer Science, 3929-3935.
