Inter- & Intra-City Image Geolocalization

Can a photo be accurately geolocated within a city from its pixels alone? While this image geolocation problem has been successfully addressed at the planetary- and nation-levels when framed as a classification problem using convolutional neural networks, no method has yet been able to precisely geolocate images within the city- and/or at the street-level when framed as a latitude/longitude regression-type problem. We leverage the highly densely sampled Streetlearn dataset of imagery from Manhattan and Pittsburgh to first develop a highly accurate inter-city predictor and then experimentally resolve, for the first time, the intra-city performance limits of framing image geolocation as a regression-type problem. We then reformulate the problem as an extreme-resolution classification task by subdividing the city into hundreds of equirectangular-scaled bins and train our respective intra-city deep convolutional neural network on tens of thousands of images. Our experiments serve as a foundation to develop a scalable inter- and intra-city image geolocation framework that, on average, resolves an image within 250 m2. We demonstrate that our models outperform SIFT-based image retrieval-type models based on differing weather patterns, lighting conditions, location-specific imagery, and are temporally robust when evaluated upon both past and future imagery. Both the practical and ethical ramifications of such a model are also discussed given the threat to individual privacy in a technocentric surveillance capitalist society.