To overcome the challenges of lack of information and regular surveys, machine learning based satellite imagery algorithms have been proposed to predict the economic indicators of a region. These indicators (ranging from poverty estimation, slum detection) not only help government design policies but are vital tools for the businesses to understand their customers, design their business strategy and evaluate their business model. Unfortunately, these algorithms’ predictions are not interpretable and being based on homogeneity, they fail to generalize over the regions. Instead of popular black-box techniques, we plan to create an interpretable economic well-being analysis using satellite imagery and geo-spatial datasets (e.g. estimating the density of buildings, closeness to parks, population prediction, etc.) and solve the domain adaptation problem by constraining over the interpretation rather than just generic image or feature level adaptation.