- Computer uses deep learning model to identify which scenery is beautiful
- Artificial intelligence software trialled rating 200,000 photos of London
- Model can distinguish between green wasteland and beautiful countryside
- It can also pick out beautiful historic buildings in a city
A group of researchers from The Alan Turing Institute and Data Science Lab at Warwick Business School have trained a computer to recognise beautiful scenery using “deep learning”, an approach to artificial intelligence which is inspired by the architecture of the human brain.
The researchers – including Turing student Chanuki Seresinhe, and Faculty Fellows Tobias Preis and Suzy Moat – took more than 200,000 images of places in the UK that had been rated for their beauty on the website Scenic-or-Not and showed them to a deep learning model in order to find out what makes a scenic location beautiful.
The deep learning model processed all 200,000 images and labelled them with information on what was in the picture, such as ‘valley’, ‘grass’, ‘no horizon’ or ‘open space’. Using these labels, the researchers were able to investigate which attributes of a scene led to higher scenic scores.
The scientists then trained a new deep learning model to look at pictures and rate them itself.
Chanuki Seresinhe, spending a year of her PhD at the Turing as part of the Institute’s doctoral enrichment scheme, said: “We tested our model in London and it not only identified parks like Hampstead Heath as beautiful, but also built-up areas such as Big Ben and the Tower of London.”
Chanuki Seresinhe, along with Turing Faculty Fellows and WBS Data Science Lab directors Suzy Moat and Tobias Preis, used the MIT Places Convolutional Neural Network – a deep learning model – to analyse the images from Scenic-or-Not, which were rated by 1.5 million people, and find what attributes, such as ‘trees’, ‘mountain’, ‘hospital’ and ‘highway’, corresponded to high and low scenic ratings.
Deep learning models are a particular kind of ‘neural network’ – simulated networks of neurons, like those in the human brain – and have driven recent dramatic advances in artificial intelligence tasks, such as facial recognition and speech recognition.
How can AI be used to idenitfy scenic locations?
Using the MIT Places deep learning model, the researchers found that features such as ‘valley’, ‘coast’, ‘mountain’ and ‘trees’ were associated with higher scenicness.
However, some man-made elements also tended to improve scores, including historical architecture such as ‘church’, ‘castle’, ‘tower’ and ‘cottage’, as well as bridge-like structures such as ‘viaduct’ and ‘aqueduct’. Interestingly, large areas of greenspace such as ‘grass’ and ‘athletic field’ led to lower ratings of scenicness rather than boosting scores.
Chanuki added: “It appears that the old adage ‘natural is beautiful’ seems to be incomplete: flat and uninteresting green spaces are not necessarily beautiful, while characterful buildings and stunning architectural features can improve the beauty of a scene.
“I am fascinated by how deep learning can help us to develop a deeper insight into what human beings collectively might understand to be beautiful.”
Will AI be able to recognise beauty?
Suzy Moat, Faculty Fellow at The Alan Turing Institute, said: ”These findings are of particular interest in the context of our previous research, which showed that people who live in areas rated as more scenic report their health to be better, even when we take data on green space into account.
“Our new results shine light on why a location being green might not be enough for it to be considered attractive. This distinction has clear relevance for planning decisions which aim to improve the wellbeing of local inhabitants.”
The scientists then adapted the deep learning model to rate the scenicness of new locations, and tested it on more than 200,000 photographs of London that the model hadn’t seen before.
Tobias Preis, Faculty Fellow at the Turing, said: “It was fascinating to see that the model understood that bridges and historical architecture increase the perceived beauty of a scene, while grass and greenery is not necessarily scenic.
“Our previous results make it clear that scientists and policymakers alike need measurements of environmental beauty, not just measurements of how green places are. Games like Scenic-or-Not can help us collect millions of ratings from humans, but having a model which can automatically tell us whether a place is beautiful or not opens up completely new horizons.”
The paper, Using deep learning to quantify the beauty of outdoor places,is published in Royal Society Open Science.
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The Alan Turing Institute