FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915944527298560 |
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| author | Gao, Shijian Liang, Jiahui Yuan, Yifeng Lu, Wenlihan Shen, Guobin Yang, Liuqing |
| author_facet | Gao, Shijian Liang, Jiahui Yuan, Yifeng Lu, Wenlihan Shen, Guobin Yang, Liuqing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17362 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking Gao, Shijian Liang, Jiahui Yuan, Yifeng Lu, Wenlihan Shen, Guobin Yang, Liuqing Signal Processing 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. |
| title | FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.17362 |