Data from: Using deep learning to quantify the beauty of outdoor places

Beautiful outdoor locations are protected by governments and have recently
been shown to be associated with better health. But what makes an outdoor
space beautiful? Does a beautiful outdoor location differ from an outdoor
location that is simply natural? Here, we explore whether ratings of over
200 000 images of Great Britain from the online game Scenic-Or-Not,
combined with hundreds of image features extracted using the Places
Convolutional Neural Network, might help us understand what beautiful
outdoor spaces are composed of. We discover that, as well as natural
features such as ‘Coast’, ‘Mountain’ and ‘Canal Natural’, man-made
structures such as ‘Tower’, ‘Castle’ and ‘Viaduct’ lead to places being
considered more scenic. Importantly, while scenes containing ‘Trees’ tend
to rate highly, places containing more bland natural green features such
as ‘Grass’ and ‘Athletic Fields’ are considered less scenic. We also find
that a neural network can be trained to automatically identify scenic
places, and that this network highlights both natural and built locations.
Our findings demonstrate how online data combined with neural networks can
provide a deeper understanding of what environments we might find
beautiful and offer quantitative insights for policymakers charged with
design and protection of our built and natural environments.