Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities

Google is getting ready to release its StreetLearn dataset for training machine-learning models to navigate cities without a map. The StreetLearn environment relies on images from Google Street View and has been used by Google DeepMind to train a software agent to navigate various western cities without reference to a map or GPS co-ordinates, using only visual clues such as landmarks as it wanders the streets. The StreetLearn environment encompasses multiple regions within the centers of the cities of London, Paris and New York. It is made up of cropped 360-degree panoramic images of street scenes from Street View, each measuring 84 x 84 pixels. Each panoramic image is a node in larger network or graph of images, with up to 65,000 nodes per 5km city region, and multiple regions per city. Each region has a distinct urban setting, for instance differing amount of construction and varying numbers of parks and bridges.