We present a new method for taking an urban scene reconstructed from a large Internet photo collection and reasoning about its change in appearance through time. Our method estimates when individual 3D points in the scene existed, then uses spatial and temporal affinity between points to segment the scene into spatio-temporally consistent clusters. The result of this segmentation is a set of spatio-temporal objects that often correspond to meaningful units, such as billboards, signs, street art, and other dynamic scene elements, along with estimates of when each existed. Our method is robust and scalable to scenes with hundreds of thousands of images and billions of noisy, individual point observations. We demonstrate our system on several large-scale scenes, and demonstrate an application to time stamping photos. Our work can serve to chronicle a scene over time, documenting its history and discovering dynamic elements in a way that can be easily explored and visualized.