精确的空中无线电环境表征对低空规划至关重要。然而，现有数据集与估计方法缺乏应对复杂空中空间所需的高分辨率粒度；当前方案还普遍存在泛化能力差、严重依赖环境先验等问题。为弥补上述不足，本文提出FARM——一种面向统一空中无线电地图估计的开创性基础模型。该模型依托一个新构建的高分辨率数据集，该数据集专为低空环境设计，涵盖多频段与多天线配置。FARM采用掩码自编码器提取空中无线电环境的深层潜在表征，并以此引导基于扩散机制的解码器，通过迭代优化生成高保真度的信号分布。大量实验表明，FARM显著优于现有最先进基准方法，并在未见场景中展现出卓越的泛化能力。最终，FARM作为低空经济的关键基础设施，赋能自主空中物流与智能城市网络。

Precise aerial radio environment characterization is vital for low-altitude planning. However, existing datasets and estimation methods lack the high-resolution granularity required for complex aerial spaces. Additionally, current schemes suffer from poor generalization and heavy reliance on environmental priors. To address these gaps, this paper introduces FARM, a pioneering foundation model for unified aerial radio map estimation. This model is supported by a newly curated, high-resolution dataset featuring multi-band and multi-antenna configurations specifically for low-altitude environments. FARM utilizes a masked autoencoder to extract deep latent representations of the aerial radio environment, which then guide a diffusion-based decoder to generate high-fidelity signal distributions through iterative refinement. Extensive experiments demonstrate that FARM significantly outperforms state-of-the-art benchmarks and exhibits superior generalization capabilities across unseen scenarios. Ultimately, FARM serves as a critical infrastructure for low-altitude economy by enabling autonomous aerial logistics and intelligent urban networking.