Description

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:

• Scene Generation: A pure Python-based pipeline for generating 3D scenes for arbitrary areas of interest.
• ML-based Propagation Model: ML-based signal coverage prediction using our pre-trained model based on the cascaded U-Net architecture described in ​this paper.

As of November 2025, v2.0.0 enhances the scene generation pipeline to include:

• LiDAR Terrain Data
• Building height calibration using Digital Elevation Models (DEMs)

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 ​Technical Report to better understand how it works.

Prerequisites

Package Installation

Demos

Cite