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.

风景优美的户外场所受政府保护，且近期研究证实，这类场所与更佳的健康状况存在关联。但究竟是什么赋予户外空间美感？风景优美的户外场所与纯粹的自然户外场所是否存在差异？
在此，我们借助在线游戏Scenic-Or-Not中超过20万张英国本土图像的景观评级数据，结合使用场所卷积神经网络（Places Convolutional Neural Network）提取的数百个图像特征，探究构成优美户外空间的要素。
研究发现，除"海岸""山地""天然运河"等自然特征之外，"塔楼""城堡""高架桥"等人工构筑物同样会提升场所的景观评级。值得注意的是，尽管包含"树木"的场景往往评级较高，但拥有"草地""运动场"等平淡无奇的自然绿地特征的场所，其景观评级反而更低。
我们还证实，可通过训练神经网络自动识别景观优美的场所，且该神经网络会同时关注自然与人工场所。本研究结果表明，结合在线数据与神经网络技术，能够深化我们对人类审美偏好环境的认知，并可为负责规划与保护人工及自然环境的政策制定者提供量化参考依据。