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

翻译：宇航员摄影图像的精确像素级地理定位对于释放这类独特遥感地球数据潜力至关重要，尤其在灾害管理与气候变化研究中的应用。近期研究虽已建立宇航员摄影定位任务，但现有方法或因部署成本过高难以大规模应用，或仅能生成过于粗略的定位结果。为此，我们提出EarthMatch——一种迭代单应性估计方法，在保持处理速度优势的同时，能够实现宇航员摄影图像的细粒度定位。我们将宇航员摄影基准数据集AIMS重新聚焦于地理定位任务本身，并在此数据集上验证了本方法的有效性。此外，我们提出了一种新的公平图像匹配器比较方法，并在定位流程中对不同匹配模型进行了全面评估。本方法将为实现450万张（且持续增长）的宇航员地球摄影图像库提供快速精准的定位支持。代码与数据网页详见 https://earthloc-and-earthmatch.github.io