We develop a machine learning based tool for accurate prediction of development and socio-economic indicators from high resolution day-time satellite imagery. The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data. We use a deep convolutional neural network to build a model for regression of asset indicators from satellite images. We show that the direct regression of asset indicators gives superior R2 scores compared to that of transfer learning through night light data, which is a popular proxy for economic development used world wide. We also use the asset prediction model for accurate transfer learning of other socio-economic and health indicators which are not intuitively related to observable features in satellite images, or are not always well correlated with each other. The tool can be extended to monitor the progress of development of a regionover time, and to flag potential anomalies because of dissimilar outcomes due to different policy interventions in a geographic region by detecting sharp spatial discontinuities in the regression output .
We develop a machine learning based tool for accurate prediction of socio-economic indicators from daytime satellite imagery. The %DIFDELCMD < ] %%% %DIFDELCMD < ] %%% diverse set of indicators are often not intuitively related to observable features in satellite images, and are not even always well correlated with each other. Our predictive tool is more accurate than using night light as a proxy, and can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and flag potential anomalies . Finally, we use predicted variables to do robustness analysis of a regression study of high rate of stunting in India .
D
development and
R
high resolution day-time
daytime
D
indicators that we use are derived from the Census 2011
D
The Ministry of Home Affairs, Government of India, 2011
D
and the NFHS-4
D
The Ministry of Health and Family Welfare, Government of India, 2016
R
survey data. We use a deep convolutional neural network to build a model for regression of asset indicators from satellite images. We show that the direct regression of asset indicators gives superior R2 scores compared to that of transfer learning through night light data, which is a popular proxy for economic development used world wide. We also use the asset prediction model for accurate transfer learning of other socio-economic and health indicators which are
diverse set of indicators are often
R
or are not
and are not even
R
The tool can be extended to monitor the progress of development of a regionover time, and to
Our predictive tool is more accurate than using night light as a proxy, and can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and
R
because of dissimilar outcomes due to different policy interventions in a geographic region by detecting sharp spatial discontinuities in the regression output
. Finally, we use predicted variables to do robustness analysis of a regression study of high rate of stunting in India