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Where was this pic taken will be answered by Google PlaNet

Where was this pic taken will be answered by Google PlaNet

Where was this picture taken The answer will now come from Google. Computer vision specialist Tobias Weyand and his colleagues at Google created a deep-learning program called PlaNet, and trained it to identify locations where photos were taken based on visual cues.

Since PlaNet is an artificial neural network, it can learn. So the team taught the network how to identify a photograph s location on the grid just using information contained in the pixels.

To test PlaNet s accuracy, Weyand and his team fed it 2.3 million geotagged Flickr images. From there, PlaNet narrowed down 48 percent of them to the right continent, 28.4 percent to the right country, 10.1 percent to the right city, and 3.6 percent to the actual street, reports Discovery.

Images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse.

In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images.

While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, the model is able to use and integrate multiple visible cues. Researchers show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, they extend model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, they demonstrate that this model achieves a 50% performance improvement over the single-image model.

Where was this pic taken will be answered by Google PlaNet