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README.md

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+# FARM Aerial Radio Map Dataset
+
+## Overview
+
+This repository releases the constructed aerial radio map datasets based on ARM-Omni for FARM training and evaluation. The datasets correspond to Table 2 of the FARM paper and include `D1-D10`, `P1`, `F1`, and `A1`.
+
+ARM-Omni is introduced in the paper as a large-scale aerial radio environment dataset. This Hugging Face release provides the ARM datasets used in the FARM experiments as downloadable scene archives. It is not a full ARM-Omni release.
+
+Paper:
+
+- https://arxiv.org/abs/2604.17362
+
+Code:
+
+- The FARM codebase will be released separately.
+
+## Dataset Coverage
+
+| 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 |
+
+`D1-D10` are used for primary training and evaluation. `P1`, `F1`, and `A1` are used for zero-shot evaluation.
+
+## 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 `.npy` file stores an aerial radio map tensor with shape:
+
+```text
+1 × H × W × W
+```
+
+The first dimension is the radio-map channel. `H` is the height axis. `W` follows the map grid size listed in Dataset Coverage. Most dataset groups use `512 × 512` spatial grids, while `P1` uses `256 × 256`.
+
+## 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
+```
+
+- For `P1`, the released map grid is `256 × 256`. When using transmitter locations recorded in the original `512 × 512` scene coordinate system, the `256 × 256` region is centered on the transmitter pixel. For convenience, the crop window can be computed as:
+
+```python
+def compute_centered_crop_window(center_row, center_col, height, width, crop_size=256):
+    crop_h = min(int(crop_size), int(height))
+    crop_w = min(int(crop_size), int(width))
+    start_y = max(0, min(int(center_row) - crop_h // 2, int(height) - crop_h))
+    start_x = max(0, min(int(center_col) - crop_w // 2, int(width) - crop_w))
+    return start_y, start_y + crop_h, start_x, start_x + crop_w
+```
+## Citation
+
+If you use this dataset, please cite the FARM 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}
+}