For UAV large oblique image geo-localization in the dense buildings area, there are still two main challenges. One is the presence of obvious occlusion and large viewpoint differences in UAV images, and the other arises from the fact that reference images, particularly orthographic satellite images, lack façade information of man-made structures (such as buildings and roads), which is crucial for UAV large oblique images. Most of existing image-based geo-localization methods only address the first challenge, neglecting the interference brought by the second challenge, especially for UAV large oblique image geo-localization in the dense buildings area. Motivated by both these two challenges, we have proposed a novel method for UAV large oblique image geo-localization in the dense buildings areas, with the segments direction statistics (SDS) features and their histogram descriptors designed. By considering both the local and global features of man-made structures, the proposed method effectively addresses the significant information difference encountered in cross-view image matching. We conducted experiments on both the public UAV images dataset University-1652 and our own collected dataset of UAV large oblique long focal whiskbroom (LO-LF-W) images. Comparative analysis with state-of-the-art (SOTA) methods demonstrated that the proposed method improves the geo-localization accuracy by approximately 10%. Furthermore, the proposed method exhibits greater robustness to noise and changing orientation of reference images, making it particularly well-suited for dense buildings areas that pose challenges for existing methods.