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

TL;DR

EarthMatch is an efficient iterative homography-based method that achieves fine-grained, pixel-level geolocalization of astronaut photographs, enhancing disaster response and climate research capabilities.

Contribution

The paper introduces EarthMatch, a novel fast and precise localization method for astronaut photography, and refocuses the AIMS benchmark for geolocalization tasks.

Findings

EarthMatch outperforms existing methods in localization accuracy.

The method maintains speed suitable for large-scale deployment.

Extensive evaluation shows effectiveness across various matching models.

Abstract

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