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Mapping Poverty through Data Integration and Artificial Intelligence
Mapping Poverty through Data Integration and Artificial Intelligence
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This special supplement to the Key Indicators for Asia and the Pacific 2020 discusses how poverty estimates can be enhanced by integrating household surveys and censuses with data extracted from satellite imagery. As part of a special ADB knowledge initiative, computer vision techniques and machine-learning algorithms were applied on datasets from the Philippines and Thailand to demonstrate increased granularity of poverty estimation using artificial intelligence. The report identifies practic…
  • Leidėjas:
  • Metai: 2021
  • Puslapiai: 54
  • ISBN-10: 9292623133
  • ISBN-13: 9789292623135
  • Formatas: 21.6 x 27.9 x 0.4 cm, minkšti viršeliai
  • Kalba: Anglų

Mapping Poverty through Data Integration and Artificial Intelligence (el. knyga) (skaityta knyga) | knygos.lt

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This special supplement to the Key Indicators for Asia and the Pacific 2020 discusses how poverty estimates can be enhanced by integrating household surveys and censuses with data extracted from satellite imagery.

As part of a special ADB knowledge initiative, computer vision techniques and machine-learning algorithms were applied on datasets from the Philippines and Thailand to demonstrate increased granularity of poverty estimation using artificial intelligence. The report identifies practical considerations and technical requirements for this novel approach to mapping the spatial distribution of poverty. It also outlines the investments required by national statistics offices to fully capitalize on the benefits of incorporating innovative data sources into conventional work programs.

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  • Leidėjas:
  • Metai: 2021
  • Puslapiai: 54
  • ISBN-10: 9292623133
  • ISBN-13: 9789292623135
  • Formatas: 21.6 x 27.9 x 0.4 cm, minkšti viršeliai
  • Kalba: Anglų

This special supplement to the Key Indicators for Asia and the Pacific 2020 discusses how poverty estimates can be enhanced by integrating household surveys and censuses with data extracted from satellite imagery.

As part of a special ADB knowledge initiative, computer vision techniques and machine-learning algorithms were applied on datasets from the Philippines and Thailand to demonstrate increased granularity of poverty estimation using artificial intelligence. The report identifies practical considerations and technical requirements for this novel approach to mapping the spatial distribution of poverty. It also outlines the investments required by national statistics offices to fully capitalize on the benefits of incorporating innovative data sources into conventional work programs.

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