Matching downward-looking unmanned aerial vehicle (UAV) images to georeferenced satellite or aerial map tiles supports map-based geo-localization when direct positioning signals are degraded or unavailable. Variations in UAV altitude change the observed ground footprint and create a scale mismatch between query images and fixed-scale map references. We address this problem with an altitude-adaptive aerial geo-localization framework. The framework estimates relative altitude from a single image using a frequency-domain representation and regression-as-classification. The predicted altitude-class center defines a scale prior that guides query normalization toward the canonical map scale before full-gallery visual place recognition. A footprint-adaptive margin classifier (FAMC) adjusts training margins according to footprint overlap. Experiments on two synthetic and two real-flight datasets show that, at the 100 m localization threshold, altitude adaptation improves average R@1 and R@5 by 50.68 and 47.20 percentage points, respectively, for our visual place recognition module compared with the same retrieval pipeline without altitude normalization. These results show that a relative-altitude-derived scale prior improves cross-altitude UAV-to-map image matching in the evaluated regions and camera settings.
Robust inertial odometry is essential for various carriers when external sensing is unreliable. Learning-based methods reduce integration drift by capturing local motion priors, but these methods often remain tied to a particular carrier, limiting generalization across heterogeneous platforms. We present MosaicIMU, a carrier-conditioned Mixture-of-Experts (MoE) pretraining-and-adaptation framework for generalizable neural inertial odometry. MosaicIMU uses a prototype-based router to compose carrier-specific expert features, decodes local velocity and uncertainty constraints, and integrates them with a history-aware EKF. For unseen domain adaptation, it freezes the pretrained base model and learns a new lightweight expert residual branch. For edge-deployment, it further reuses the router to select informative online samples for efficient incremental updates. Experiments show that MosaicIMU consistently outperforms learning-based baselines, reducing average ATE and RTE-10s by 40% and 34%, respectively. These results highlight that MosaicIMU provides a scalable pretraining-to-deployment paradigm for generalizable and adaptive neural inertial odometry.