Identifying whether there has been a significant change in a scene from a set of images is an important practical task, and has received much attention. The problem has been, however, that although existing statistical techniques perform reasonably well, it has been impossible to achieve the high levels of accuracy demanded by most real applications. This is due to the fact that changes in pixel intensity are not a particularly good indicator of significant change in a scene. We propose a semantic change detection approach which aims to classify the content of an image before attempting to identify change. This technology builds upon recent developments in large-scale classification which have dramatically improved both accuracy and speed.