Research Article

Combining satellite imagery and machine learning to predict poverty

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Science  19 Aug 2016:
Vol. 353, Issue 6301, pp. 790-794
DOI: 10.1126/science.aaf7894

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  • Precedents in combining satellite imagery and machine learning to predict socieconomic information
    • Francisco J Tapiador, Dean of Environmental Sciences and Biochemistry, University of Castilla-La Mancha (UCLM). Toledo, Spain.

    The paper by Jean et al. (2016) reports ‘a novel machine learning approach for extracting socioeconomic data from high-resolution daytime satellite imagery’. As mentioned by Blumenstock (2016) this avenue of research has potential for start fighting poverty with data. The approach, however, is not new. Our 2008 paper entitled ‘Deriving fine-scale socioeconomic information of urban areas using very high-resolution satellite imagery’ (Tapiador et al. 2011) was first in proposing a method for combining daytime satellite imagery and machine learning to derive such information in an even more precise way. Jean et al. paper and Blumestock’s comment fail to acknowledge this and other direct precedents such as Lo and Faber (1997) and Jensen and Cowen (1999). Proper, deep investigation of previous work and a fair acknowledgment of precedents are two of the cornerstones of science; otherwise, we all would be continuously reinventing the wheel. I hope the authors amend the omissions in further research.


    Blumenstock, J.E., 2016. Fighting poverty with data. Science 353, (6301) 753-754.

    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 (6301), 790-794.

    Jensen, J.R. and Cowen, D.C., 1999. Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogrammetric Engineering and Remote Sensing, 65, pp. 611–622.


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    Competing Interests: None declared.

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