We develop a predictive machine learning based tool for accurate regression of development and socio-economic indicators from high resolution daytime satellite imagery. The indicators  are derived from  Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and  NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data. We use a deep convolutional neural network to build a model for accurate prediction of asset indicators from satellite images. We show that the direct regression of asset indicators is more accurate than transfer learning through night light data, which is a popular proxy for economic development used world wide. We  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  . The tool can be extended to monitor the progress of development of a region over 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 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] and the NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] 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 region over 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.
D
predictive
R
regression
prediction
R
daytime
day-time
A
that we use
A
the
A
the
R
accurate prediction
regression
R
is more accurate than
gives superior R2 scores compared to that of
A
also
A
, or are not always well correlated with each other