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Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Rev. Income Wealth 59, S9–S15
Devarajan · 2013
Earlier work this paper cites.
Poor numbers: How we are misled by african development statistics and what to do about it
Jerven · 2013
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2014
Earlier work this paper cites.
Recent Trends in Satellite Image Pan-sharpening techniques
K. Kpalma, M. Chikr El-Mezouar, and N. Taleb · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Povcalnet online poverty analysis tool, http:// iresearch.worldbank.org/povcalnet/
World Bank · 2015
Cited alongside, same era.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Combining satellite imagery and machine learning to predict poverty
N. Jean, M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon · 2016
Later among the works it cites.
https://landsat.usgs.gov
Landsat 7: Description of Spectral Bands · 2017
Closest in time.
https://landsat.usgs.gov/panchromatic-image-sharpening-landsat-7-etm
Panchromatic Image Sharpening of Landsat 7 ETM+ · 2017
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[Online; accessed 9-June-2017]
Version 4 dmsp-ols nighttime lights time series · 2017
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