[[Include(WikiToC)]]

=== 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 [https://github.com/functions-lab/geo2sigmap/tree/main 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 [https://ieeexplore.ieee.org/document/10632773 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 [https://arxiv.org/abs/2504.21719 Technical Report] to better understand how it works. Tutorials for Sionna can be found [here https://nvlabs.github.io/sionna/rt/tutorials.html].

=== Prerequisites ===

To use Geo2SigMap with ease, we **strongly** suggest managing python packages using Anaconda. Anaconda's package manager, [https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html conda], offers everything you need with no extra configuration. It becomes extremely useful while running the notebook examples in the [wiki:Demos] section.

The installation process should ensure that all required packages are installed. In the event of any issue, this is the [https://github.com/bndr/pipreqs pipreqs] list as of 1/21/2025:

{{{#!div style="font-size: 100%"
  {{{#!python
  osmnx >= 2.0.0
  numpy
  pyproj
  shapely
  rasterio
  tqdm
  pillow
  open3d
  }}}
}}}

Given the large overhead for ray tracing and ML tasks, //access to a dedicated GPU// (with RT cores) is also suggested. Using Sionna RT with a CPU offers limited performance and will restrict the capabilities for experimentation.

=== Package Installation ===

1. Create the Conda Environment:

   {{{
   #!sh
   conda create --yes --name g2sm --channel conda-forge pdal python=3.12
   conda activate g2sm
   pip install pyvista==0.45.2
   }}}

   If you opt not to use conda, please note the additional installation of pyvista v0.45.2

2. Clone and Install geo2sigmap:

   {{{
   #!sh
   git clone https://github.com/functions-lab/geo2sigmap
   cd geo2sigmap/package
   pip install .
   }}}

   The package is now installed and can be called via the CLI tool **or** using the Python function API. Details for both are included below.

=== Creating a Scene ===

The easiest way to create a scene is using the [https://github.com/functions-lab/geo2sigmap/tree/main/package CLI Tool]. Scene boundaries are defined by parameterized GPS coordinates. To find these coordinates (either a singular corner or all four), [http://bboxfinder.com/#0.000000,0.000000,0.000000,0.000000 bbox] is a helpful service. You can zoom and scroll through the world map to find your target area. Once found, the **box icon** allows you to draw the rectangular area and save the coordinates.

=== Generating a Coverage Map with Sionna RT ===

=== Generating a Coverage Map using Cascaded U-Net Model ===

==== Cite ====