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Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain

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  • Ola Hall
  • Mattias Ohlsson
  • Thortseinn Rognvaldsson

Abstract

Recent advances in artificial intelligence and machine learning have created a step change in how to measure human development indicators, in particular asset based poverty. The combination of satellite imagery and machine learning has the capability to estimate poverty at a level similar to what is achieved with workhorse methods such as face-to-face interviews and household surveys. An increasingly important issue beyond static estimations is whether this technology can contribute to scientific discovery and consequently new knowledge in the poverty and welfare domain. A foundation for achieving scientific insights is domain knowledge, which in turn translates into explainability and scientific consistency. We review the literature focusing on three core elements relevant in this context: transparency, interpretability, and explainability and investigate how they relates to the poverty, machine learning and satellite imagery nexus. Our review of the field shows that the status of the three core elements of explainable machine learning (transparency, interpretability and domain knowledge) is varied and does not completely fulfill the requirements set up for scientific insights and discoveries. We argue that explainability is essential to support wider dissemination and acceptance of this research, and explainability means more than just interpretability.

Suggested Citation

  • Ola Hall & Mattias Ohlsson & Thortseinn Rognvaldsson, 2022. "Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain," Papers 2203.01068, arXiv.org.
  • Handle: RePEc:arx:papers:2203.01068
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    References listed on IDEAS

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    1. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.
    2. Neumann, Kathleen & Verburg, Peter H. & Stehfest, Elke & Müller, Christoph, 2010. "The yield gap of global grain production: A spatial analysis," Agricultural Systems, Elsevier, vol. 103(5), pages 316-326, June.
    3. John Gibson, 2016. "Poverty Measurement: We Know Less than Policy Makers Realize," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 3(3), pages 430-442, September.
    4. Martin Ravallion, 2020. "On Measuring Global Poverty," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 167-188, August.
    5. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    6. Charlotta Mellander & José Lobo & Kevin Stolarick & Zara Matheson, 2015. "Night-Time Light Data: A Good Proxy Measure for Economic Activity?," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-18, October.
    7. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    8. Bob Baulch & John Hoddinott, 2000. "Economic mobility and poverty dynamics in developing countries," Journal of Development Studies, Taylor & Francis Journals, vol. 36(6), pages 1-24.
    9. Brock Smith & Samuel Wills, 2018. "Left in the Dark? Oil and Rural Poverty," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 5(4), pages 865-904.
    10. Wilhelm Östberg & Olivia Howland & Joseph Mduma & Dan Brockington, 2018. "Tracing Improving Livelihoods in Rural Africa Using Local Measures of Wealth: A Case Study from Central Tanzania, 1991–2016," Land, MDPI, vol. 7(2), pages 1-26, April.
    11. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    12. Keola, Souknilanh & Andersson, Magnus & Hall, Ola, 2015. "Monitoring Economic Development from Space: Using Nighttime Light and Land Cover Data to Measure Economic Growth," World Development, Elsevier, vol. 66(C), pages 322-334.
    13. Watmough, Gary R. & Atkinson, Peter M. & Saikia, Arupjyoti & Hutton, Craig W., 2016. "Understanding the Evidence Base for Poverty–Environment Relationships using Remotely Sensed Satellite Data: An Example from Assam, India," World Development, Elsevier, vol. 78(C), pages 188-203.
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    Cited by:

    1. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.

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