Using SatelliteDatato GuideUrban Poverty Reduction

Publikation: KonferencebidragPaperForskning

Standard

Using SatelliteDatato GuideUrban Poverty Reduction. / Sohnesen, Thomas Pave; Fisker, Peter Kielberg; Malmgren-Hansen, David.

2019. Paper præsenteret ved IARIW-World Bank Conference, Washington, DC, USA.

Publikation: KonferencebidragPaperForskning

Harvard

Sohnesen, TP, Fisker, PK & Malmgren-Hansen, D 2019, 'Using SatelliteDatato GuideUrban Poverty Reduction', Paper fremlagt ved IARIW-World Bank Conference, Washington, DC, USA, 07/11/2019 - 08/11/2019. <http://iariw.org/washington/Sohnesen-paper.pdf>

APA

Sohnesen, T. P., Fisker, P. K., & Malmgren-Hansen, D. (2019). Using SatelliteDatato GuideUrban Poverty Reduction. Paper præsenteret ved IARIW-World Bank Conference, Washington, DC, USA. http://iariw.org/washington/Sohnesen-paper.pdf

Vancouver

Sohnesen TP, Fisker PK, Malmgren-Hansen D. Using SatelliteDatato GuideUrban Poverty Reduction. 2019. Paper præsenteret ved IARIW-World Bank Conference, Washington, DC, USA.

Author

Sohnesen, Thomas Pave ; Fisker, Peter Kielberg ; Malmgren-Hansen, David. / Using SatelliteDatato GuideUrban Poverty Reduction. Paper præsenteret ved IARIW-World Bank Conference, Washington, DC, USA.16 s.

Bibtex

@conference{72a3cf3f38284ba190853b16ad62748b,
title = "Using SatelliteDatato GuideUrban Poverty Reduction",
abstract = "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.",
author = "Sohnesen, {Thomas Pave} and Fisker, {Peter Kielberg} and David Malmgren-Hansen",
year = "2019",
language = "English",
note = " IARIW-World Bank Conference ; Conference date: 07-11-2019 Through 08-11-2019",

}

RIS

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