EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911941870485504 |
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| author | Berton, Gabriele Goletto, Gabriele Trivigno, Gabriele Stoken, Alex Caputo, Barbara Masone, Carlo |
| author_facet | Berton, Gabriele Goletto, Gabriele Trivigno, Gabriele Stoken, Alex Caputo, Barbara Masone, Carlo |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05422 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography Berton, Gabriele Goletto, Gabriele Trivigno, Gabriele Stoken, Alex Caputo, Barbara Masone, Carlo Computer Vision and Pattern Recognition 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 |
| title | EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.05422 |