tures are learned without direct supervision, in contrastto previous efforts to extract features from aerial imagery,which have relied heavily on large amounts of expert-labeleddata, e.g., labeled examples of roads (Mnih and Hinton2010; 2012). To conﬁrm the semantics of the ﬁlters, we vi-sualize their activations for the same set of images (Figure 2,right). These maps conﬁrm our interpretation by identifyingthe image parts that are most responsible for activating theﬁlter. For example, the ﬁlter in the third row mostly acti-vates on road segments. These features are extremely usefulsocioeconomic indicators and suggest that transfer learningto the poverty task is possible.