We propose a methodology for predicting subnational economic development using daytime satellite imagery. We collected high-resolution satellite images and corresponding ground truth data (e.g. buildings, roads) for over 28 million 1km x 1km grid cells covering 25 European and 7 African countries. We first use a standard random forest model to identify a subset of features from the ground truth data that best predict fixed capital at the EU NUTSII level.
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