in domains with images fundamentally different from nor-mal “human-eye view” images typically resort to curating anew, speciﬁc dataset such as Places205 (Zhou et al. 2014).In contrast, our transfer learning approach does not requirehuman annotation and is much more scalable. Additionally,unsupervised approaches such as autoencoders may wasterepresentational capacity on irrelevant features, while thenighttime light labels guide learning towards features rele-vant to wealth and economic development.