Modelo del canal de propagación

Recent advances in computer vision, particularly the development of CNNs, are allowing us to extract insights from images at a far greater speed and accuracy than ever before. We explore to what level of accuracy we can create a CNN model to predict the beauty of scenes for which we either do not have crowdsourced scenic ratings, or for which we require scenic ratings at a higher resolution. We use a transfer learning approach and modify the existing Places365 CNN in order to create new CNNs to predict the scenicness of images. We achieve the best

performance with our Scenic-Or-Not CNNtrained using images from the Scenic-Or-

Not dataset, which are imagesoriginally sourced from Geograph, using the VGG16 convolutional neural network architecture (performance scores are 0.658 for all images and 0.590 for our urban built-up images).

We also explore how our Scenic-Or-Not CNN performs on images from another

source, Google Street View, as this allows us to gather a far more comprehensive

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However, further training of this CNN using Google Street View images improves

our performance score to 0.435 (using the GoogLeNet neural network architecture).

We suggest possible reasons why the accuracy for Google Street View images might still be lower than the accuracy for Geograph images. Google Street View

images are often of a lower quality than Geograph images, because Google Street

View images are often composites that contain image artefacts such as blurred areas. Furthermore, the CNN's knowledge is based on training using several million

images that have primarily been sourced via search engines (Google Images, Bing

Images, and Flickr) rather than composite images from Google Street View. Thus, the CNN's training is largely based on images that might be not have been shot in

the wide angle of view common to Google Street View images.

We present our predictions for images in London from both our scenic CNNs (our Scenic-Or-Not CNN and our Street-View-Scenic CNN), and find that they are broadly in line with intuition. Our Scenic-Or-Not CNN predicts high ratings for images containing primarily natural elements, such as those located in London parks known for their attractive scenery, such as Hampstead and Richmond Park, and also predicts high scenic ratings for beautiful buildings, such as the iconic Big Ben and the Tower of London. The Street-View-Scenic CNN also picks up on paths in parks as being Scenic. Interestingly, this CNN seems to find areas in north, south and west London more beautiful compared to central and east London.

We also explore to what degree we can track changes in scenicness over time. Initial inspection of the data suggests that outer areas of London may have become less scenic while areas in central and east London have become more scenic, but the changes in scenicness are small. Further analysis would be required to ascertain to what extent the measured changes represent actual changes to the design of areas in London, or circumstantial changes such as differences in weather, obstruction of views, or temporary construction.

Observing dramatic changes to areas in London using Google Street View

imagery is also challenging due to the fact that some of the biggest changes are often focused on very compact areas. For example, over the last ten years, the regeneration of the Lower Lea Valley area in East London for the 2012 Olympics and the redevelopment of the Kings Cross area next to the St. Pancras Eurostar station have made remarkable changes to London, but each in its own small area. Thus, in order to track changes in scenicness over time, a far more comprehensive set of images, at an even higher resolution than already gathered, may be needed, including ones taken in public outdoor areas inaccessible to cars. For example, Granary Square in Kings Cross is a beautiful pedestrianised public space featuring

choreographed water fountains. Gaining imagery of such areas may soon be

possible thanks to Google’s increasing use of Street View cameras on backpacks,

scooters and tricycles to augment their image database to cover new types of locations.

Nonetheless, analyses using convolutional neural networks have helped us to dramatically improve our models to estimate the scenicness of our environment. Our research shows that beauty – once though to be in the eye of the beholder and thus an area of investigation impenetrable by computers – can in fact be decoded by computer algorithms. We argue that the ability to estimate scenicness at large scale and at speed using neural networks opens up new avenues for future social science research to investigate the connection between the beauty of the environment and various aspects of human life, from our wellbeing to the economic prosperity of a city.