| | 4 | |
| | 5 | Geo2SigMap is an efficient framework for high-fidelity RF signal mapping leveraging geographic databases, ray tracing, and a novel cascaded U-Net model. The project offers an automated and scalable pipeline that efficiently generates 3D building and path gain (PG) maps. The repository is split into two distinct partitions: |
| | 6 | |
| | 7 | * Scene Generation: A pure Python-based pipeline for generating 3D scenes for arbitrary areas of interest. |
| | 8 | * ML-based Propagation Model: ML-based signal coverage prediction using our pre-trained model based on the cascaded U-Net architecture described in [https://ieeexplore.ieee.org/document/10632773 this paper]. |
| | 9 | |
| | 10 | As of November 2025, v2.0.0 enhances the scene generation pipeline to include: |
| | 11 | |
| | 12 | * LiDAR Terrain Data |
| | 13 | * Building height calibration using Digital Elevation Models (DEMs) |
| | 14 | |
| | 15 | This drastically improves the accuracy of the environment being processed by the ML-based Propagation Model or a ray tracer of your choice. Throughout the following notebook examples, we utilize Sionna RT. This package is open-source and highly accurate for generating coverage maps. If you are unfamiliar with Sionna RT, feel free to read Nvidia's [https://arxiv.org/abs/2504.21719 Technical Report] to better understand how it works. |
