Cross-view geo-localization (CVGL) estimates a camera's location by matching a street-view image to geo-referenced overhead imagery, enabling GPS-denied localization and navigation. Existing methods almost universally formulate CVGL as an image-retrieval problem in a contrastively trained embedding space. This ties performance to large batches and hard negative mining, and it ignores both the geometric structure of maps and the coverage mismatch between street-view and overhead imagery. In particular, salient landmarks visible from the street view can fall outside a fixed satellite crop, making retrieval targets ambiguous and limiting explicit spatial inference over the map. We propose Just Zoom In, an alternative formulation that performs CVGL via autoregressive zooming over a city-scale overhead map. Starting from a coarse satellite view, the model takes a short sequence of zoom-in decisions to select a terminal satellite cell at a target resolution, without contrastive losses or hard negative mining. We further introduce a realistic benchmark with crowd-sourced street views and high-resolution satellite imagery that reflects real capture conditions. On this benchmark, Just Zoom In achieves state-of-the-art performance, improving Recall@1 within 50 m by 5.5% and Recall@1 within 100 m by 9.6% over the strongest contrastive-retrieval baseline. These results demonstrate the effectiveness of sequential coarse-to-fine spatial reasoning for cross-view geo-localization.

翻译：跨视角地理定位（CVGL）通过将街景图像与地理参考的俯视影像进行匹配来估计相机位置，从而实现无GPS环境下的定位与导航。现有方法几乎普遍将CVGL建模为对比学习嵌入空间中的图像检索问题。这种范式不仅依赖大批量训练与难负样本挖掘，还忽略了地图的几何结构以及街景与俯视影像之间的覆盖不匹配问题。具体而言，街景中可见的显著地标可能落在固定卫星裁剪区域之外，导致检索目标模糊并限制了对地图的显式空间推理。我们提出"Just Zoom In"（仅需放大）方法，通过在城市级俯视地图上进行自回归式放进来实现CVGL。该模型从粗粒度卫星视图出发，执行短序列的放大决策以选择目标分辨率下的终端卫星单元，无需对比损失或难负样本挖掘。我们进一步引入了一个反映真实拍摄条件的实用基准测试集，包含众包街景图像与高分辨率卫星影像。在该基准测试中，Just Zoom In实现了最先进的性能：与最强对比检索基线相比，50米范围内的Recall@1提升了5.5%，100米范围内的Recall@1提升了9.6%。这些结果证明了从粗到细的序列化空间推理在跨视角地理定位中的有效性。