ISS-Geo142：国际空间站宇航员摄影地理定位基准测试集
ISS-Geo142: A Benchmark for Geolocating Astronaut Photography from the International Space Station
AI总结：
ISS-Geo142为国际空间站宇航员摄影的地理定位提供了基准测试集，包含142张图像及三种不同方法的评估结果，其中TerraByte实现了90%的准确率。
摘要：
本文介绍了ISS-Geo142，一个精心编纂的基准测试集，用于国际空间站（ISS）拍摄的宇航员摄影的地理定位。尽管拍摄时ISS的精确位置已知，但这些图像中所描绘的具体地球位置通常没有直接地理参考，这使得自动化定位变得复杂。ISS-Geo142包含142张图像及其相关元数据和手动确定的地理坐标，涵盖多种空间尺度和场景类型。在该基准测试集之上，我们实现了并评估了三种地理定位流程：基于神经网络的方法（NN-Geo）使用VGG16特征和在地图衍生的感兴趣区域（AOIs）上的交叉相关，一个基于尺度不变特征变换的流程（SIFT-Match）使用滑动窗口特征匹配在拼接的高分辨率AOIs上，以及TerraByte，一个基于GPT-4模型的AI系统，该系统结合图像内容和ISS坐标进行推理。在ISS-Geo142上，NN-Geo在我们的评估协议下对75.52%的图像实现了匹配，SIFT-Match在结构丰富的场景上实现了高精度，但计算成本较高，而TerraByte建立了最强的整体基准，正确地对约90%的图像进行了地理定位，同时还能生成可读的地理描述。方法和实验最初是在2023年开发的；本文是经过修订和扩展的版本，将工作置于后续跨视图地理定位和遥感视觉-语言模型的进展之中。综合来看，ISS-Geo142和这三个流程为未来的ISS图像地理定位工作提供了具体的、有历史依据的基准测试。
英文摘要：
This paper introduces ISS-Geo142, a curated benchmark for geolocating astronaut photography captured from the International Space Station (ISS). Although the ISS position at capture time is known precisely, the specific Earth locations depicted in these images are typically not directly georeferenced, making automated localization non-trivial. ISS-Geo142 consists of 142 images with associated metadata and manually determined geographic locations, spanning a range of spatial scales and scene types. On top of this benchmark, we implement and evaluate three geolocation pipelines: a neural network based approach (NN-Geo) using VGG16 features and cross-correlation over map-derived Areas of Interest (AOIs), a Scale-Invariant Feature Transform based pipeline (SIFT-Match) using sliding-window feature matching on stitched high-resolution AOIs, and TerraByte, an AI system built around a GPT-4 model with vision capabilities that jointly reasons over image content and ISS coordinates. On ISS-Geo142, NN-Geo achieves a match for 75.52\% of the images under our evaluation protocol, SIFT-Match attains high precision on structurally rich scenes at substantial computational cost, and TerraByte establishes the strongest overall baseline, correctly geolocating approximately 90\% of the images while also producing human-readable geographic descriptions. The methods and experiments were originally developed in 2023; this manuscript is a revised and extended version that situates the work relative to subsequent advances in cross-view geo-localization and remote-sensing vision--language models. Taken together, ISS-Geo142 and these three pipelines provide a concrete, historically grounded benchmark for future work on ISS image geolocation.
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