Using SatelliteDatato GuideUrban Poverty Reduction
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Using SatelliteDatato GuideUrban Poverty Reduction. / Sohnesen, Thomas Pave; Fisker, Peter Kielberg; Malmgren-Hansen, David.
2019. Paper presented at IARIW-World Bank Conference, Washington, DC, United States.Research output: Contribution to conference › Paper › Research
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TY - CONF
T1 - Using SatelliteDatato GuideUrban Poverty Reduction
AU - Sohnesen, Thomas Pave
AU - Fisker, Peter Kielberg
AU - Malmgren-Hansen, David
PY - 2019
Y1 - 2019
N2 - Povertyreductionis increasingly an urbanchallenge, and a challenge that continues to be hampered by lack of data. One such example is the urban social safety net program implemented by the Government of Mozambique, that is spatial in nature, but works without any data on the within-city spatial distribution of poverty. The lack of detailed dataon poverty is commonin many developing as well as middle-income countries. This study appliesConvolutional Neural Networks onhigh-resolutionsatelliteimagesof cities in Mozambique,andcombines theiroutputs with householdlevel geo-referenced survey data. The results show that readily available data sources can generate detailed neighborhood-levelpoverty maps, providing key operational guidance for implementation of the urban social safety net.Importantly,the approach ishighly automatic, applicable at scale, and cost-effective. It is thus a key step forward in the application of remote sensing image recognition for urban poverty reduction.
AB - Povertyreductionis increasingly an urbanchallenge, and a challenge that continues to be hampered by lack of data. One such example is the urban social safety net program implemented by the Government of Mozambique, that is spatial in nature, but works without any data on the within-city spatial distribution of poverty. The lack of detailed dataon poverty is commonin many developing as well as middle-income countries. This study appliesConvolutional Neural Networks onhigh-resolutionsatelliteimagesof cities in Mozambique,andcombines theiroutputs with householdlevel geo-referenced survey data. The results show that readily available data sources can generate detailed neighborhood-levelpoverty maps, providing key operational guidance for implementation of the urban social safety net.Importantly,the approach ishighly automatic, applicable at scale, and cost-effective. It is thus a key step forward in the application of remote sensing image recognition for urban poverty reduction.
M3 - Paper
T2 - IARIW-World Bank Conference
Y2 - 7 November 2019 through 8 November 2019
ER -
ID: 248555358