  The appearance of the world varies dramatically not only from place to place
but also from hour to hour and month to month. Every day billions of images
capture this complex relationship, many of which are associated with precise
time and location metadata. We propose to use these images to construct a
global-scale, dynamic map of visual appearance attributes. Such a map enables
fine-grained understanding of the expected appearance at any geographic
location and time. Our approach integrates dense overhead imagery with location
and time metadata into a general framework capable of mapping a wide variety of
visual attributes. A key feature of our approach is that it requires no manual
data annotation. We demonstrate how this approach can support various
applications, including image-driven mapping, image geolocalization, and
metadata verification.
