Scenes, like objects, are visual entities that can be categorized into functional and semantic groups. One of the core concepts of human categorization is the idea that category membership is graded: some exemplars are more typical than others. Here, we obtain human typicality rankings for more than 120,000 images from 706 scene categories through an online rating task on Amazon Mechanical Turk. We use these rankings to identify the most typical examples of each scene category. Using computational models of scene classification based on global image features, we find that images which are rated as more typical examples of their category are more likely to be classified correctly. This indicates that the most typical scene examples contain the diagnostic visual features that are relevant for their categorization. Objectless, holistic representations of scenes might serve as a good basis for understanding how semantic categories are defined in term of perceptual representations.