EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography

Gabriele Berton, Gabriele Goletto, Gabriele Trivigno, Alex Stoken, Barbara Caputo, Carlo Masone; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4264-4274

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. Code and data are available at https://EarthLoc-and-EarthMatch.github.io/