# Dataset of Pathloss and ToA Radio Maps With Localization Application

arXiv: https://arxiv.org/abs/2212.11777 · HTML: https://mlsanity.com/paper/2212.11777/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments](https://mlsanity.com/paper/2105.07158/) (2021)
- [Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach](https://mlsanity.com/paper/2106.12556/) (2021)
- [LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning](https://mlsanity.com/paper/2202.00738/) (2022)

## Built on by (most cited first, 27 of 27)

- [A Scalable and Generalizable Pathloss Map Prediction](https://mlsanity.com/paper/2312.03950/) (2023)
- [Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps](https://mlsanity.com/paper/2408.15252/) (2024)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [The First Pathloss Radio Map Prediction Challenge](https://mlsanity.com/paper/2310.07658/) (2023)
- [Radio Map Prediction from Aerial Images and Application to Coverage Optimization](https://mlsanity.com/paper/2410.17264/) (2024)
- [Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments](https://mlsanity.com/paper/2402.00878/) (2024)
- [CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors](https://mlsanity.com/paper/2504.17323/) (2025)
- [Radio Map Estimation via Latent Domain Plug-and-Play Denoising](https://mlsanity.com/paper/2501.13472/) (2025)
- [Deep Learning-Based CKM Construction with Image Super-Resolution](https://mlsanity.com/paper/2411.08887/) (2024)
- [RadioFormer: A Multiple-Granularity Radio Map Estimation Transformer with 1textpertenthousand Spatial Sampling](https://mlsanity.com/paper/2504.19161/) (2025)
- [Bayesian-Driven Graph Reasoning for Active Radio Map Construction](https://mlsanity.com/paper/2508.09142/) (2025)
- [RadioFlow: Efficient Radio Map Construction Framework with Flow Matching](https://mlsanity.com/paper/2510.09314/) (2025)
- [Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations](https://mlsanity.com/paper/2512.02757/) (2025)
- [On the Effective Usage of Priors in RSS-based Localization](https://mlsanity.com/paper/2212.00728/) (2022)
- [RadioKMoE: Knowledge-Guided Radiomap Estimation with Kolmogorov-Arnold Networks and Mixture-of-Experts](https://mlsanity.com/paper/2511.16986/) (2025)
- [RadioMapMotion: A Dataset and Baseline for Proactive Spatio-Temporal Radio Environment Prediction](https://mlsanity.com/paper/2511.17526/) (2025)
- [RMSup: Physics-Informed Radio Map Super-Resolution for Compute-Enhanced Integrated Sensing and Communications](https://mlsanity.com/paper/2512.10965/) (2025)
- [The Vienna 4G/5G Drive-Test Dataset](https://mlsanity.com/paper/2603.02638/) (2026)
- [Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation](https://mlsanity.com/paper/2604.05520/) (2026)
- [Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches](https://mlsanity.com/paper/2604.22056/) (2026)
- [Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm](https://mlsanity.com/paper/2605.28234/) (2026)
- [RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling](https://mlsanity.com/paper/2605.29538/) (2026)
- [AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey](https://mlsanity.com/paper/2606.23125/) (2026)
- [Multi-Modal Conditioned High-Resolution Transformer for Urban Electromagnetic Field Map Prediction Download PDF](https://mlsanity.com/paper/2606.27671/) (2026)
- [EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning](https://mlsanity.com/paper/2607.16449/) (2026)
- [Physics-informed VAE-EVT for Tail Aware Radio Map Prediction](https://mlsanity.com/paper/2608.15314/) (2026)
- [Physics-Unrolled Neural Operator for Wireless Field Modeling](https://mlsanity.com/paper/2608.18495/) (2026)

## Abstract (arXiv)

In this article, we present a collection of radio map datasets in dense urban setting, which we generated and made publicly available. The datasets include simulated pathloss/received signal strength (RSS) and time of arrival (ToA) radio maps over a large collection of realistic dense urban setting in real city maps. The two main applications of the presented dataset are 1) learning methods that predict the pathloss from input city maps (namely, deep learning-based simulations), and, 2) wireless localization. The fact that the RSS and ToA maps are computed by the same simulations over the same city maps allows for a fair comparison of the RSS and ToA-based localization methods.