Aerial-ground Cross-modal Localization: Dataset, Ground-truth, and Benchmark

Accurate visual localization in dense urban environments poses a fundamental task in photogrammetry, geospatial information science, and robotics. While imagery is a low-cost and widely accessible sensing modality, its effectiveness on visual odometry is often limited by textureless surfaces, severe viewpoint changes, and long-term drift. The growing public availability of airborne laser scanning (ALS) data opens new avenues for scalable and precise visual localization by leveraging ALS as a prior map. However, the potential of ALS-based localization remains underexplored due to three key limitations: (1) the lack of platform-diverse datasets, (2) the absence of reliable ground-truth generation methods applicable to large-scale urban environments, and (3) limited validation of existing Image-to-Point Cloud (I2P) algorithms under aerial-ground cross-platform settings. To overcome these challenges, we introduce a new large-scale dataset that integrates ground-level imagery from mobile mapping systems with ALS point clouds collected in Wuhan, Hong Kong, and San Francisco.