Video: 30 seconds of animated geolocation data for street art images taken around the world over time pic.twitter.com/YzTjdN2sLY— Leonard Bogdonoff (@rememberlenny) November 2, 2018
Bogdonoff's commentary on that video:
Using the hundreds of thousands of images I was able to crawl from Instagram before the geographical data was made inaccessible, I analyzed how the presence of street art around the world, over time. [...] This data, which was no longer associated to the actual images that were originally indexed — due to Instagram’s change in policy — provided insight into the presence of street art and graffiti around the world.
Interestingly, the image frequency also provided a visual which eludes to an obvious relationship between urban centers and street art. If this was analyzed further there may be clear correlations between street art and real estate value, community social ties, political engagement, and other social phenomena.
In the past few days, I have focused on synthesizing the various means with which I expect to use machine learning for analyzing street art. Because of the media’s misrepresentation of artificial intelligence and the broad meaning of machine learning in the technical/marketing field, I was struggling with what I meant myself.
Prior to this project’s incarnation, I had thought it would be possible to build out object detection models to recognize different types of graffiti in images. For example, an expression of vandalism is different than a community sanctioned mural. I also imagined it would be possible to build out ways of identifying specific letters in larger letter-form graffiti pieces. I believe it would be interesting to combine the well defined labels and data set with a variational auto-encoder to generate machine learning based letter-form pieces.
Going further, I thought it would be possible to use machine learning to detect when an image in a place was “new”, based on it not having been detected in previous images from a specific place. I thought it would also be interesting to find camera feeds to railway cars traveling across the US and build out a pipeline for capturing the graffiti on train cars, identifying the train cars serial number, and tracking how train cars and their respective art traveled the country.
And so it goes.