--- 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/scene_.tar.gz ``` After extraction, the scene contains .npy ARM tensors organized as: ```text ////tx_yaw.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 //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} }