Before revision:  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.
After revision:  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 diverse set of indicators are often not intuitively related to observable features in satellite images, or are not always well correlated with each other.