发表机构
LTCI, Télécom Paris, Institut Polytechnique de Paris; SATIE, École normale supérieure Paris-Saclay, CNRS, Université Paris-Saclay（巴黎理工学院电信学院; 巴黎萨克雷高等师范学院法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有EMF数据集缺乏真实基站和连续城市覆盖的问题，提出UrbanEMF，一个基于真实城市几何与基站部署、通过射线追踪构建的城市尺度EMF映射数据集，支持多接收高度及RSS与路径损耗地图，为大规模城市无线环境的学习方法提供基准。
AI 中文摘要
电磁场（EMF）映射对于无线传播建模至关重要，并且是广泛应用的基石，包括频谱感知、网络规划以及集成感知与通信（ISAC）。然而，现有数据集通常局限于空间分离的二维场景，缺乏真实基站（BS）信息。为弥补这一空白，我们提出了UrbanEMF，一个利用射线追踪技术、基于真实城市几何和物理基站部署构建的城市尺度电磁场映射数据集。与传统基于单一场景的数据集不同，UrbanEMF保留了连续的城市尺度覆盖和真实的发射机位置信息。它进一步通过纳入多个接收机高度来扩展传统的二维地图。此外，数据集同时提供了聚合接收信号强度（RSS）地图和路径损耗地图，以表征无线电环境互补的方面。这项工作为在大规模城市无线环境中开发和评估基于学习的方法提供了一个真实且灵活的基准。本工作的代码可在以下网址获取：this https URL
英文摘要
Electromagnetic Fields (EMF) mapping is essential for wireless propagation modeling and are fundamental to a wide range of applications, including spectrum awareness, network planning, and integrated sensing and communication (ISAC). However, existing datasets are often limited to spatially separated 2D scenes without real base station (BS) information. To address this gap, we present UrbanEMF, a city-scale EMF mapping dataset constructed from real world urban geometry and physical BS deployments using ray tracing. Unlike conventional single scene-based datasets, UrbanEMF preserves continuous city-scale urban coverage and realistic transmitter location information. It further extends traditional 2D maps by incorporating multiple receiver heights. In addition, both aggregated received signal strength (RSS) maps and path loss maps are provided to characterize complementary aspects of the radio environment. This work provides a realistic and flexible benchmark for developing and evaluating learning-based methods in large-scale urban wireless environments. The code for this work is available at: https://github.com/lemonstudy/UrbanEMF
