Most current methods address this problem by implicitly learning cross-view correspondences to regress camera localization [5, 13, 22, 24]. These approaches establish aggregated descriptor-based representations for both ground and aerial images, resulting in relatively short inference time. However, these methods suffer from two main issues. Firstly, they lack an explicit establishment of spatial unification between views and heavily rely on brute-force training to simultaneously solve spatial and semantic alignment. Secondly, in theory, precise relative pose estimation requires obtaining local cross-view matching relationships, such as point-to-point correspondences. Implicit cross-view feature correlation and pose regression may lead to a loss in localization accuracy. In contrast, methods[13] based on point-to-point matches have been proposed, which utilize LM optimization for feature map alignment between the ground and satellite views. These methods often leverage end-to-end ground truth pose as supervision to learn local correspondences between cross-views. Such supervision may be weak for point-wise deep feature metric learning. Based on the above observations, we propose a method that explicitly represents point-wise correspondences between cross-views, thus facilitating accurate pose estimation. Our key idea is to learn dense flow fields between cross-views and directly compute accurate ground camera poses from such flows in an end-to-end manner. To achieve this, we develop two distinct convolutional neural networks for feature extraction from the aerial and ground images, respectively. But ground and aerial feature maps exhibit significant domain differences in terms of spatial configuration and visual appearance, posing a critical challenge for the neural network to learn effective matching on such feature maps. Furthermore, due to the absence of depth information in the ground images, direct cross-view matching leads to a one-to-many correspondence issue. For instance, a building facade in the ground image may correspond to multiple edges in the aerial image. Existing methods often assume a fixed ground height to enable the projection from the ground to the satellite image[13]. In line with this approach, our method employs the same strategy to geometrically project the ground features, obtaining a bird's eye view (BEV) feature map that corresponds to the ground image. This projection facilitates spatial alignment between the cross-view feature maps. As mentioned above, for the uncertainty in the ground height assumption, we introduce a RefineBlock to further refine the BEV features and achieve content alignment. Subsequently, optical flow estimation is performed on the refined BEV feature map and the satellite feature map using flow decoder networks based on RAFT[17]. Specifically, we calculate multi-scale correlation volumes using the BEV and satellite features and employ GRU modules iteratively for dense flow estimation. Furthermore, we assign scores to each established matching relationship based on their similarity, effectively reducing the weight of erroneous matches. A notable advantage of computing flows on the BEV and satellite features is that the matching relationship allows us to directly utilize the least square method to solve the 3 DoF pose, i.e., the