SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models

Abstract

Aerial 6DoF localization typically relies on precise GNSSsignals or radiometrically rich 3D reconstructions, limiting scalability andon-board deployment. We propose SemCityLoc, a semantic–geometricalignment system that reframes aerial pose estimation as structuredsurface registration between foundation-model-derived visual priors andstandardized LoD-compliant 3D city models. Instead of matching sparsecontours or dense texture, our method aligns semantic surfaces and monoc-ular depth with lightweight semantic 3D building models, increasing pose* Corresponding authordiscriminability in repetitive and occluded urban environments. To enableaccurate evaluation, we introduce SemCityLockeD, the first real-worldbenchmark combining centimeter-accurate UAV poses with standardizedLoD1–LoD3 semantic city models and challenging low-altitude imagery.Experiments demonstrate substantial improvements over existing map-based approaches, improving recall by up to 36% and reducing meanpositional error from 9.89 m to 2.62 m in challenging urban canyons. Ourresults indicate that semantically structured geometry provides sufficientand scalable constraints for high-precision aerial localization withoutradiometric scene reconstructions. The code and data are available athttps://albertchen98.github.io/SemCityLoc.