| --- |
| license: mit |
| pretty_name: FARM Aerial Radio Map Dataset |
| task_categories: |
| - image-to-image |
| - feature-extraction |
| tags: |
| - radio-map |
| - aerial-networking |
| - wireless-communication |
| - low-altitude-networking |
| - foundation-model |
| - simulation |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
| # FARM Aerial Radio Map (ARM) Dataset |
|
|
| ## Paper: |
|
|
| - FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking (https://arxiv.org/abs/2604.17362) |
|
|
| ## Overview |
|
|
| This repository releases the constructed ARM datasets based on ARM-Omni for FARM training, in-domain evaluation (`D1-D10`), and zero-shot evaluation (`P1`, `F1`, and `A1`). The dataset coverage is summarized below: |
|
|
|
|
| | Dataset | Frequencies (GHz) | Max Rx Height (m) | Beamwidths | Map Grid Size | Volume | |
| | --- | --- | ---: | --- | --- | ---: | |
| | D1 | 2.1, 3.3, 5.9 | 120 | 30°, 120°, Iso | 512 × 512 | 15000 | |
| | D2 | 3.5, 4.9, 5.9 | 130 | 30°, 120°, Iso | 512 × 512 | 15000 | |
| | D3 | 2.1, 4.9, 5.9 | 110 | 30°, 120° | 512 × 512 | 15000 | |
| | D4 | 3.3, 3.5, 4.9 | 110 | 30°, 120°, Iso | 512 × 512 | 15000 | |
| | D5 | 3.3, 4.9, 5.9 | 120 | 30°, 120°, Iso | 512 × 512 | 15000 | |
| | D6 | 2.1, 3.3, 3.5 | 130 | 30°, 120° | 512 × 512 | 15000 | |
| | D7 | 2.1, 3.3, 3.5, 5.9 | 130 | 30°, Iso | 512 × 512 | 15000 | |
| | D8 | 2.1, 3.5, 4.9, 5.9 | 100 | 120°, Iso | 512 × 512 | 15000 | |
| | D9 | 2.1, 3.3, 3.5, 4.9 | 120 | 30°, 120° | 512 × 512 | 15000 | |
| | D10 | 2.1, 3.3, 3.5, 4.9, 5.9 | 130 | 30°, 120°, Iso | 512 × 512 | 15000 | |
| | P1 | 2.1, 3.3, 3.5, 4.9, 5.9 | 150 | 30°, 120°, Iso | 256 × 256 | 15000 | |
| | F1 | 2.6, 7.1 | 120 | 30°, 120°, Iso | 512 × 512 | 15000 | |
| | A1 | 2.1, 3.3, 3.5, 4.9, 5.9 | 120 | 60° | 512 × 512 | 15000 | |
|
|
| A fuller ARM-Omni release is planned for a future update. |
|
|
| ## Repository Layout |
|
|
| Each scene is stored as a compressed archive on the Hub: |
|
|
| ```text |
| <dataset>/scene_<scene_id>.tar.gz |
| ``` |
|
|
| After extraction, the scene contains .npy ARM tensors organized as: |
|
|
| ```text |
| <dataset>/<scene_id>/<frequency>/<antenna_pattern>/tx<ID>_yaw<VALUE>.npy |
| ``` |
|
|
| Example: |
|
|
| ```text |
| D1/7/freq59/iso/tx45_yaw0.npy |
| ``` |
|
|
| Scene IDs are not globally continuous across dataset groups. Users should rely on the extracted folder names as the authoritative scene IDs. |
|
|
| ## Data Format |
|
|
| Each released raw `.npy` file stores an ARM volume with shape: |
|
|
| ```text |
| 1 × 30 × 512 × 512 |
| ``` |
|
|
| The dimensions correspond to one radio-map channel, 30 height layers, and a `512 × 512` spatial grid. During data loading, different input sizes can be obtained by selecting a subset of height layers and applying a spatial crop centered on the transmitter pixel. For example, this procedure is used to obtain the `256 × 256` crop for `P1`. |
|
|
| ## Signal Encoding and Scene Metadata |
|
|
| - The no-signal truncation threshold is `-120 dB`. In the encoded `uint8` representation, this threshold corresponds to pixel value `130`. |
| - Building occupancy is embedded in the released ARM tensors; in each height-layer radio map, pixels with value `0` correspond to building-occupied regions. |
| - The transmitter location metadata is stored in the scene-level CSV file: |
|
|
| ```text |
| <dataset>/<scene_id>/tx_positions_512.csv |
| ``` |
|
|
| ````md |
| ## Citation |
| |
| If you use this dataset, please cite our paper: |
| |
| ```text |
| S. Gao, J. Liang, Y. Yuan, W. Lu, G. Shen, and L. Yang, "FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking," arXiv preprint arXiv:2604.17362, 2026. |
| |
| @article{gao2026farm, |
| author = {Gao, Shijian and Liang, Jiahui and Yuan, Yifeng and Lu, Wenlihan and Shen, Guobin and Yang, Liuqing}, |
| title = {{FARM}: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking}, |
| journal = {arXiv preprint arXiv:2604.17362}, |
| year = {2026} |
| } |
| |
