  Precise, pixel-wise geolocalization of astronaut photography is critical to
unlocking the potential of this unique type of remotely sensed Earth data,
particularly for its use in disaster management and climate change research.
Recent works have established the Astronaut Photography Localization task, but
have either proved too costly for mass deployment or generated too coarse a
localization. Thus, we present EarthMatch, an iterative homography estimation
method that produces fine-grained localization of astronaut photographs while
maintaining an emphasis on speed. We refocus the astronaut photography
benchmark, AIMS, on the geolocalization task itself, and prove our method's
efficacy on this dataset. In addition, we offer a new, fair method for image
matcher comparison, and an extensive evaluation of different matching models
within our localization pipeline. Our method will enable fast and accurate
localization of the 4.5 million and growing collection of astronaut photography
of Earth. Webpage with code and data at
https://earthloc-and-earthmatch.github.io
