The article addresses the complex problem of cross-view geo-localization between drone and satellite imagery, emphasizing significant challenges such as viewpoint discrepancies and the presence of hard negatives. It critiques current methodologies that employ static weighting schemes, noting that these approaches can result in unstable convergence during training and produce noisy gradients. These issues hinder the effectiveness of geo-localization models in accurately matching images from different perspectives. By highlighting these limitations, the article underscores the need for more adaptive strategies that can better handle the inherent difficulties of cross-view matching. This discussion aligns with ongoing research efforts focused on improving robustness and accuracy in geo-localization tasks. The article thus contributes to the broader discourse on enhancing machine learning techniques for spatial recognition across diverse imaging modalities.