Different cities look different. A new piece of software can tell you how. It isolates the visual elements that make a city distinct.
For this study, the researchers started with 25,000 randomly selected visual elements from city images gathered from Google Street View. A machine learning program then analyzed these visual elements to determine which details made them different from similar visual elements in other cities. After several iterations, the software identified the top-scoring patches for identifying a city. For Paris, those patches corresponded to doors, balconies, windows with railings, street signs (the shape and color of the signs, not the street names on the signs), and special Parisian lampposts. It had more trouble identifying geo-informative elements for U.S. cities, which the researches attributed to the relative lack of stylistic coherence in American cities with their melting pot of styles and influences.
This sort of visual mapping has some potential. It allows you to see what makes your city unique (or not) and can, probably/eventually, even be applied to people to see what stylistic elements create the distinctive fashions of different regions.
In short, one of the most important functions of the artist –making a place visible to itself– now has machine competition. Or eventually will. For now, this is a tool. The human the mutation.
pic nicked from here