except the survey model in every measure except recall. No-tably, the transfer model outperforms all combinations offeatures from the source problems, implying that transferlearning was successful in learning novel and useful fea-tures. Remarkably, our transfer model based on remotelysensed data approaches the performance of the survey modelbased on data expensively collected in the ﬁeld. As a sanitycheck, we ﬁnd that using simple traditional computer visionfeatures such as HOG and color histograms only achievesslightly better performance than random guessing. This fur-ther afﬁrms that the transfer learning features are nontrivialand contain information more complex than just edges andcolors.To understand the high recall of the lights model, we