# Geo2SigMap: High-Fidelity RF Signal Mapping Using Geographic Databases

arXiv: https://arxiv.org/abs/2312.14303 · HTML: https://mlsanity.com/paper/2312.14303/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2110.01848/) (2021)
- [Spatial Signal Strength Prediction using 3D Maps and Deep Learning](https://mlsanity.com/paper/2011.03597/) (2020)
- [A First Look at Commercial 5G Performance on Smartphones](https://mlsanity.com/paper/1909.07532/) (2019)
- [Millimeter Wave Channel Modeling via Generative Neural Networks](https://mlsanity.com/paper/2008.11006/) (2020)
- [A Comparison Study of Cellular Deployments in Chicago and Miami Using Apps on Smartphones](https://mlsanity.com/paper/2108.00453/) (2021)
- [Directional Measurements in Urban Street Canyons from Macro Rooftop Sites at 28 GHz for 90% Outdoor Coverage](https://mlsanity.com/paper/1908.00512/) (2019)
- [Hermes: Decentralized Dynamic Spectrum Access System for Massive Devices Deployment in 5G](https://mlsanity.com/paper/2101.02963/) (2021)
- [A Measurement Study of the Impact of Adjacent Channel Interference between C-band and CBRS](https://mlsanity.com/paper/2304.07690/) (2023)

## Built on by (most cited first, 6 of 6)

- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach](https://mlsanity.com/paper/2608.14599/) (2026)
- [RadioKMoE: Knowledge-Guided Radiomap Estimation with Kolmogorov-Arnold Networks and Mixture-of-Experts](https://mlsanity.com/paper/2511.16986/) (2025)
- [NeuralEmu: in situ Measurement-Driven, ML-based, High-Fidelity 5G Network Emulation](https://mlsanity.com/paper/2604.26080/) (2026)
- [Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion](https://mlsanity.com/paper/2607.01777/) (2026)
- [KODAMA: Multimodal Digital Twin Reconstruction for Urban RF Propagation Modelling](https://mlsanity.com/paper/2609.07298/) (2026)

## Abstract (arXiv)

Radio frequency (RF) signal mapping, which is the process of analyzing and predicting the RF signal strength and distribution across specific areas, is crucial for cellular network planning and deployment. Traditional approaches to RF signal mapping rely on statistical models constructed based on measurement data, which offer low complexity but often lack accuracy, or ray tracing tools, which provide enhanced precision for the target area but suffer from increased computational complexity. Recently, machine learning (ML) has emerged as a data-driven method for modeling RF signal propagation, which leverages models trained on synthetic datasets to perform RF signal mapping in "unseen" areas. In this paper, we present Geo2SigMap, an ML-based framework for efficient and high-fidelity RF signal mapping using geographic databases. First, we develop an automated framework that seamlessly integrates three open-source tools: OpenStreetMap (geographic databases), Blender (computer graphics), and Sionna (ray tracing), enabling the efficient generation of large-scale 3D building maps and ray tracing models. Second, we propose a cascaded U-Net model, which is pre-trained on synthetic datasets and employed to generate detailed RF signal maps, leveraging environmental information and sparse measurement data. Finally, we evaluate the performance of Geo2SigMap via a real-world measurement campaign, where three types of user equipment (UE) collect over 45,000 data points related to cellular information from six LTE cells operating in the citizens broadband radio service (CBRS) band. Our results show that Geo2SigMap achieves an average root-mean-square-error (RMSE) of 6.04 dB for predicting the reference signal received power (RSRP) at the UE, representing an average RMSE improvement of 3.59 dB compared to existing methods.