figure 1. Annotated map of north America.

Flickr. 7, 9, 21 Our algorithms for analyzing the world at a global scale can automatically create annotated world maps by finding the most photographed cities and landmarks, inferring place names from text tags, and analyzing the images themselves to identify “ canonical” images to summarize each place. Figures 1 and 2 show examples of such maps. Figure 1 is an annotated map of North America, automatically generated by analyzing nearly 35 million photos from Flickr. For each of the top 30 most photographed cities, the map shows the name of the city inferred from tags, the name of the most photographed landmark, and a representative photo of the landmark. Figure 2 is an automatically generated annotated map of Europe.

This analysis can also generate
statistics about places, such as rank-
ing landmarks by their popularity or
studying which kinds of users visit
which sites. At a more local level, we
can use automatic techniques from
computer vision to produce strikingly
accurate 3D models of a landmark, giv-
en a large number of 2D photos taken
by many different users from many dif-
ferent vantage points. Figure 3 shows
an example 3D reconstruction of the
Colosseum created completely auto-
matically from photos harvested from
the Internet. This figure shows the 3D
model itself, along with the position
and orientation of the camera that
took each photo. The reconstructed
cameras are shown as black wireframe
pyramids indicating where each photo
was taken, and the Colosseum is re-
constructed as a dense 3D point cloud,
similar to what a laser scanner would
capture—but in this case, reconstruct-
ed completely automatically from pho-
tos found on the Internet.