network by inspecting the 25 maximally activating imagesfor each ﬁlter (Figure 2, left and the companion technicalreport for more visualizations (Xie et al. 2015)). Activationlevels for ﬁlters in the middle of the network are obtained bypassing the images forward through the ﬁlter, applying theReLU nonlinearity, and then averaging the map of activationvalues. We ﬁnd that many ﬁlters learn to identify semanti-cally meaningful features such as urban areas, water, roads,barren land, forests, and farmland. Amazingly, these fea-