- We will send in 10–14 business days.
/images/books/13598387/9789292623135.jpg)
- Publisher: Asian Development Bank
- Year: 2021
- Pages: 54
- ISBN-10: 9292623133
- ISBN-13: 9789292623135
- Format: 21.6 x 27.9 x 0.4 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Mapping Poverty through Data Integration and Artificial Intelligence (e-book) (used book) | bookbook.eu
Reviews
Description
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 19d.23:45:41
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Publisher: Asian Development Bank
- Year: 2021
- Pages: 54
- ISBN-10: 9292623133
- ISBN-13: 9789292623135
- Format: 21.6 x 27.9 x 0.4 cm, softcover
- Language: English English
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.
Reviews