S urf N e HF: Surface-Aware Neural Height Fields for Efficient 3D Urban Mapping Using Crowdsourced GNSS Data

Modern cities increasingly rely on 3D urban maps as digital infrastructures to support applications such as navigation, autonomous driving, urban development, and cellular network planning. Maintaining these maps with high fidelity and timely updates, however, remains challenging due to the high cost and limited availability of aerial imagery or LiDAR data. In this work, we present SurfNeHF, a lightweight deep learning framework that accurately infers building shapes from noisy crowdsourced GNSS data. By adapting optical inverse rendering to the radio frequency domain, SurfNeHF represents building surfaces as neural height fields and explicitly models signal-surface interactions. This surface-aware formulation enables the framework to effectively disentangle building geometry from surface reflectance and satellite sources, significantly reducing optimization complexity compared to volumetric approaches. Extensive evaluations demonstrate that SurfNeHF consistently outperforms state-of-the-art methods across diverse urban morphologies. Our framework achieves an average reconstruction accuracy of 94.6% while reducing computational overhead by 80% to 90% compared to baselines. With both higher data and system efficiency, SurfNeHF makes periodic large-scale urban map updates more practically feasible.