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

Gabriele Berton, Gabriele Goletto, Gabriele Trivigno, Alex Stoken, Barbara Caputo +1 more
5/8/2024
cs.CV

No official code found. Best implementation: gmberton/EarthMatch (38 stars, last commit 2026-03-15).

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

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Code Implementations(1)

gmberton/EarthMatchCommunity100%

Code for EarthMatch (CVPR 2024 IMW), an iterative coregistration pipeline to localize astronaut photos of Earth

382PythonMar 24, 20247 months ago
computer-visionvisual-localizationvisual-place-recognition

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Cite this paper

@article{berton2024earthmatch,
  title  = {EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography},
  author = {Gabriele Berton and Gabriele Goletto and Gabriele Trivigno and Alex Stoken and Barbara Caputo and Carlo Masone},
  year   = {2024},
  eprint = {2405.05422},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.05422v2}
}

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