  While natural beauty is often considered a subjective property of images, in
this paper, we take an objective approach and provide methods for quantifying
and predicting the scenicness of an image. Using a dataset containing hundreds
of thousands of outdoor images captured throughout Great Britain with
crowdsourced ratings of natural beauty, we propose an approach to predict
scenicness which explicitly accounts for the variance of human ratings. We
demonstrate that quantitative measures of scenicness can benefit semantic image
understanding, content-aware image processing, and a novel application of
cross-view mapping, where the sparsity of ground-level images can be addressed
by incorporating unlabeled overhead images in the training and prediction
steps. For each application, our methods for scenicness prediction result in
quantitative and qualitative improvements over baseline approaches.
