address the trade-off between ﬁxed image size and informa-tion loss from image scaling, we use a fully convolutionalmodel that takes advantage of the full satellite image. Weshow that transfer learning is successful in learning featuresrelevant not only for nighttime light prediction but also forpoverty mapping. For instance, the model learns ﬁlters iden-tifying man-made structures such as roads, urban areas, andﬁelds without any supervision beyond nighttime lights, i.e.,without any labeled examples of roads or urban areas (Fig-