---
license: cc-by-nc-4.0
task_categories:
- object-detection
tags:
- autonomous-driving
- lidar
- radar
- thermal
- multimodal
- 3d-detection
pretty_name: USEFUL
size_categories:
- 100G<n<1T
language:
- en
---

# USEFUL: mUltimodal Sensing and pErception For autonomoUs vehicLes

USEFUL is a multimodal autonomous driving perception dataset featuring **10 synchronized sensor streams** — LiDAR, 2x Radar, 3x RGB cameras, Thermal (LWIR), SWIR, and Polarimetric — paired with accurate **3D and 2D bounding box annotations** and **GPS/INS ego-pose** data. It is designed to advance research in challenging perception conditions (night, glare, fog, rain) where passive imaging alone is insufficient.

<div align="center">
<p align="center">
  <img src="media/sample.png" width="800">
</p>
</div>

---

## Key Statistics

| | |
|---|---|
| **Total size** | ~345 GB |
| **Scenes** | 83 |
| **Samples** | 10,953 |
| **Sensor streams** | 9 synchronized channels |
| **3D annotations** | 86,662 |
| **2D annotations** | 636203 |
| **Instances** | 6274 |
| **Object categories** | 10 |
| **Splits** | train / val / test |

---

## Sensor Modalities

| Channel | Modality | Description |
|---------|----------|-------------|
| `LIDAR` | LiDAR | 3D point cloud, XYZIRGB format |
| `RADAR_LEFT` | Radar | Left-side radar with Doppler velocity |
| `RADAR_RIGHT` | Radar | Right-side radar with Doppler velocity |
| `WIDE_LEFT` | RGB Camera | Wide-angle left camera |
| `NARROW` | RGB Camera | High-resolution narrow front camera |
| `WIDE_RIGHT` | RGB Camera | Wide-angle right camera |
| `LWIR` | Thermal | Long-wave infrared (thermal) camera |
| `SWIR` | SWIR | Shortwave infrared camera |
| `POLARIMETRIC` | Polarimetric | Full Stokes polarimetric camera (DOLP, AOLP, RGB 0°/45°/90°/135°) |

The LiDAR sensor used is not a conventional rotatory LiDAR. It is an `L3CAM`, produced by [Beamagine S.L.](https://beamagine.com/), multimodal embbeded system containing a thermal camera, a polarimetric camera and a MEMs-based quasi-solid state LiDAR that produce high-density point clouds within a FOV of (60º, 20º).

<div align="center">
<p align="center">
  <img src="media/fov_cotxe.png" width="600">
</p>
</div>

---

## Object Categories

`pedestrian` · `vehicle.car` · `vehicle.motorcycle` · `vehicle.construction` · `vehicle.bus` · `vehicle.truck` · `sign` · `bicycle` · `personal_mobility` · `animal`

---

## Data Structure

### Repository layout

```
GerardDMG/USEFUL/
├── shards/          # Raw sensor data — WebDataset TAR archives (.tar)
└── v1.0/            # Metadata — 12 JSON tables
```

- **`shards/`** — WebDataset TAR archives containing the raw sensor files (point clouds and images) for each sample.
- **`v1.0/`** — Twelve JSON files, one per metadata table. These do not require decompression.

### Metadata tables (`v1.0/`)

Every record is uniquely identified by a **32-character hex token**. Tables are linked to each other via these tokens.

| Table | Description |
|-------|-------------|
| `log` | Top-level recording sessions (vehicle, date, weather, location) |
| `scene` | Short clips within a log; linked list of samples |
| `sample` | Synchronized multimodal frame; linked list via `next`/`prev` |
| `sample_data` | One sensor file per channel per sample |
| `sample_annotation` | 3D bounding box in the LiDAR/ego frame |
| `sample_annotation_2d` | 2D bounding box in the camera frame |
| `sensor` | Sensor metadata (modality + channel name) |
| `calibrated_sensor` | Extrinsic and intrinsic calibration per sensor |
| `instance` | Object identity tracked across frames |
| `category` | Object class definitions |
| `visibility` | Per-annotation visibility level (1–4) |
| `ego_pose` | Vehicle GPS/INS/IMU pose at each sample timestamp |

### Sensor file naming convention

```
{vehicle}-{datetime}__{CHANNEL}__{timestamp_us}.{ext}
```

Example:
```
u001-2024-11-19-12-46-25+0100__LIDAR__1732020526668479.pcd
u001-2024-11-19-12-46-25+0100__NARROW__1732020526668479.png
```

File formats: `.pcd` for LiDAR/Radar point clouds, `.png` for all camera modalities except for POLARIMETRIC, which is .bmp.

<div align="center">
<p align="center">
  <img src="media/USEFUL_database.png" width="600">
</p>
</div>


---

## Download

Install the HuggingFace Hub library and use `snapshot_download` to fetch the dataset:

```python
from huggingface_hub import snapshot_download

# Download the full dataset (~345 GB)
snapshot_download(
    repo_id="GerardDMG/USEFUL",
    repo_type="dataset",
    local_dir="./USEFUL/shards/",
    local_dir_use_symlinks=False
)
```

#### Selective download (skip sensors you don't need)

Use `ignore_patterns` to skip specific sensor modalities and save disk space:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="GerardDMG/USEFUL",
    repo_type="dataset",
    ignore_patterns=["*SWIR*", "*WIDE_LEFT*", "*NARROW*"],  # skip these sensors
    local_dir="./USEFUL/shards/",
    local_dir_use_symlinks=False
)
```

Valid pattern keywords correspond to sensor channel names: `LIDAR`, `RADAR_LEFT`, `RADAR_RIGHT`, `WIDE_LEFT`, `NARROW`, `WIDE_RIGHT`, `LWIR`, `SWIR`, `POLARIMETRIC`.

---

## Decompression

Each sensor's data is distributed as **split multi-part archives** with optional MD5 checksums for integrity verification:

```
shards/
├── LIDAR.tar.aa
├── LIDAR.tar.ab
├── LIDAR.tar.md5
├── NARROW.tar.aa
├── NARROW.tar.ab
├── NARROW.tar.md5
└── ...
```

Use the provided `decompress_useful.py` script to reconstruct and extract the archives. It automatically verifies MD5 integrity (if checksum files are present) before extracting.

```bash
# Extract all sensors
python decompress_useful.py

# Extract specific sensors only
python decompress_useful.py --sensors LIDAR NARROW LWIR
```

By default, files are extracted to a `samples/` directory. The script handles both plain (`.tar.*`) and gzip-compressed (`.tar.gz.*`) split archives.

The metadata in `v1.0/` consists of plain JSON files and requires no decompression.

After extraction, point the devkit at the directory containing `v1.0/` as the `dataroot`.

---

## Useful-devkit

The **USEFUL devkit** provides a Python API to load, query, visualize, and export all dataset content.

**GitHub:** [https://github.com/GDMG99/useful-devkit](https://github.com/GDMG99/useful-devkit)

```bash
git clone https://github.com/GDMG99/useful-devkit.git
cd useful-devkit
pip install -e .
```

---

## License

This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license. **Non-commercial use only.**

---
## Acknowledgements

This work would not have been possible without the open-source works of [nuScenes](https://github.com/nutonomy/nuscenes-devkit) and [Truckscenes](https://github.com/TUMFTM/truckscenes-devkit).

---

## Citation

If you use the USEFUL dataset in your research, please cite using our DOI.


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<p align="center">
  <img src="media/useful_cotxe.JPG" width="600">
</p>
</div>