  We introduce a new landmark recognition dataset, which is created with a
focus on fair worldwide representation. While previous work proposes to collect
as many images as possible from web repositories, we instead argue that such
approaches can lead to biased data. To create a more comprehensive and
equitable dataset, we start by defining the fair relevance of a landmark to the
world population. These relevances are estimated by combining anonymized Google
Maps user contribution statistics with the contributors' demographic
information. We present a stratification approach and analysis which leads to a
much fairer coverage of the world, compared to existing datasets. The resulting
datasets are used to evaluate computer vision models as part of the the Google
Landmark Recognition and RetrievalChallenges 2021.
