Localization within a global reference frame is critical for the safe operation of UAVs. It is typically realized through GNSS measurements, however when signals are jammed, spoofed, occluded or reflected, this approach can lead to errors or fail. As most UAVs are equipped with cameras, absolute visual localization using georeferenced map representations offers a promising alternative. The recent invention of Gaussian Splatting introduces new opportunities for this task, leveraging real-time rendering from novel views to establish 2D-3D correspondences for pose estimation. In this work, we investigate the use of Gaussian Splatting maps for absolute visual localization of UAVs with a particular focus on geometric accuracy and its impact on the accuracy of position estimation. Through experiments with real-world data, we show that an initialization with dense Structure from Motion point clouds does not improve geometric accuracy compared to sparse initialization under the current training scheme. Additionally, constraining the position optimization of Gaussian Splats shows potential for improved pose estimation but introduces challenges during training. Despite these limitations, our results demonstrate the feasibility of Gaussian Splatting-based absolute visual localization for UAVs.