EarthLoc: Astronaut Photography Localization by Indexing Earth from
  Space
Gabriele Berton Alex Stoken Barbara Caputo Carlo Masone
Astronaut photography, spanning six decades of human spaceflight, presents a
unique Earth observations dataset with immense value for both scientific
research and disaster response. Despite its significance, accurately localizing
the geographical extent of these images, crucial for effective utilization,
poses substantial challenges. Current manual localization efforts are
time-consuming, motivating the need for automated solutions. We propose a novel
approach - leveraging image retrieval - to address this challenge efficiently.
We introduce innovative training techniques, including Year-Wise Data
Augmentation and a Neutral-Aware Multi-Similarity Loss, which contribute to the
development of a high-performance model, EarthLoc. We develop six evaluation
datasets and perform a comprehensive benchmark comparing EarthLoc to existing
methods, showcasing its superior efficiency and accuracy. Our approach marks a
significant advancement in automating the localization of astronaut
photography, which will help bridge a critical gap in Earth observations data.
Code and datasets are available at https://github.com/gmberton/EarthLoc