---
license: cc-by-nc-4.0
pretty_name: USEFUL
task_categories:
  - object-detection
tags:
  - autonomous-driving
  - multimodal
  - lidar
  - radar
  - thermal
  - swir
  - polarimetric
  - 3d-object-detection
  - adverse-weather
size_categories:
  - 10K<n<100K
viewer: false
---

<div align="center">

# USEFUL

**m<u>U</u>ltimodal <u>S</u>ensing and p<u>E</u>rception <u>F</u>or autonom<u>U</u>s vehic<u>L</u>es**

A driving dataset with nine synchronised sensor streams (LiDAR, radar, RGB, thermal, SWIR and
polarimetric) and 3D/2D box annotations, recorded in clear weather, rain, snow, fog, glare and at night.

[**Devkit**](https://github.com/GDMG99/useful-devkit) ·
[**Download**](#download) ·
[**Data format**](#data-format) ·
[**License**](#license) ·
[**Citation**](#citation)

<img src="https://huggingface.co/datasets/GerardDMG/USEFUL/resolve/main/media/useful_hero.jpg" width="100%" alt="One synchronised USEFUL sample in central Barcelona: wide RGB camera with projected LiDAR and 3D boxes, bird's-eye LiDAR view, and the narrow RGB, thermal, SWIR and polarimetric (DoLP, AoLP) channels">

</div>

## At a glance

|                         |                                                        |
|-------------------------|--------------------------------------------------------|
| Release                 | `v1.0`                                                 |
| Sensor streams          | 9 (1 LiDAR, 2 radar, 6 cameras)                        |
| Recording logs / scenes | 75 / 83                                                |
| Annotated samples       | 10,953 (all keyframes, all sensors)                    |
| 3D boxes                | 86,662, across 6,274 tracked instances                 |
| 2D boxes                | 636,203, per camera                                    |
| Object classes          | 12                                                     |
| Ego motion              | GPS / INS / IMU / heading, per sample and as per-log CSVs |
| Size                    | ≈ 230 GB (pick sensors and splits to download less)    |
| Region                  | Catalonia (city, rural road, highway)                  |
| Captured                | November 2024 – September 2025                         |

## Sensors

| Channel        | Modality                  | Format | Notes                                                    |
|----------------|---------------------------|--------|----------------------------------------------------------|
| `LIDAR`        | LiDAR                     | `.pcd` | Beamagine L3CAM, MEMS quasi-solid-state, 60° × 20° FOV, XYZ + intensity + RGB |
| `RADAR_LEFT`   | Radar                     | `.pcd` | Left corner, with Doppler velocity                       |
| `RADAR_RIGHT`  | Radar                     | `.pcd` | Right corner, with Doppler velocity                      |
| `NARROW`       | RGB                       | `.png` | Front, narrow FOV, 2064 × 1544                           |
| `WIDE_LEFT`    | RGB                       | `.png` | Front-left, wide FOV, 2064 × 1544                        |
| `WIDE_RIGHT`   | RGB                       | `.png` | Front-right, wide FOV, 2064 × 1544                       |
| `LWIR`         | Thermal (long-wave IR)    | `.png` | 320 × 240                                                |
| `SWIR`         | Short-wave IR             | `.png` | 640 × 512                                                |
| `POLARIMETRIC` | Polarimetric              | `.bmp` | 2448 × 2048 raw polarisation mosaic (0°/45°/90°/135°) → DoLP, AoLP, RGB |

All channels are synchronised per sample and calibrated: `calibrated_sensor` gives every
sensor's extrinsics (and, for cameras, intrinsics) relative to the ego vehicle.

<div align="center">
<img src="https://huggingface.co/datasets/GerardDMG/USEFUL/resolve/main/media/fov_cotxe.png" width="560" alt="Mounting position and field of view of each sensor">
</div>

## Conditions and splits

Splits are made per scene, so no scene is shared between splits.

| Split   | Scenes | Samples |
|---------|-------:|--------:|
| `train` |     55 |   7,245 |
| `val`   |      8 |   1,085 |
| `test`  |     20 |   2,623 |

| Weather       | Scenes |   | Location | Scenes |
|---------------|-------:|---|----------|-------:|
| clear         |     41 |   | city     |     41 |
| rain          |     10 |   | road     |     28 |
| snow          |     10 |   | highway  |     14 |
| night         |     10 |   |          |        |
| fog           |      9 |   |          |        |
| solar glare   |      3 |   |          |        |

Each `log` record also has a `weather_conditions` block: temperature, humidity, precipitation,
irradiance, visibility, sun elevation, and day/twilight/night. These values come from the
nearest weather stations and ERA5 reanalysis, not from sensors on the vehicle.

## Annotations

3D boxes are given in the LiDAR / ego frame (centre, size, quaternion) with LiDAR point counts.
2D boxes are given per camera image, with a visibility level (`0–40 %`, `40–60 %`,
`60–80 %`, `80–100 %`). Instances are tracked across the frames of a scene.

| Class                      | 3D boxes |
|----------------------------|---------:|
| `vehicle.car`              |   37,095 |
| `sign`                     |   23,902 |
| `pedestrian`               |   12,697 |
| `vehicle.truck`            |    4,084 |
| `vehicle.motorcycle`       |    3,650 |
| `bicycle`                  |    3,408 |
| `vehicle.bus`              |    1,035 |
| `personal_mobility`        |      408 |
| `animal`                   |      265 |
| `vehicle.construction`     |      110 |
| `vehicle.motorcycle.crowd` |        8 |
| `bicycle.crowd`            |        0 |

## Privacy

Faces and licence plates are blurred in every image of the `NARROW`, `WIDE_LEFT`,
`WIDE_RIGHT`, `POLARIMETRIC` and `SWIR` channels: 53,185 regions in 25,569 images. Before
release, every region was checked automatically against the original image, and a random
sample was checked by eye. `LWIR` (320 × 240 thermal) shows neither readable plates nor
identifiable faces, so it is not blurred.

If you find a face or plate that is still recognisable, please open a discussion on this
repository with the file name, and we will fix it. Do not try to re-identify anyone in the data.

## Download

The data is split into one self-contained `.tar` per **sensor × scene**, under
`shards/<CHANNEL>/<split>/<scene>.tar`. Each tar can be downloaded and extracted on its own,
so you only fetch the sensors and splits you need.

```bash
pip install -U huggingface_hub

# Everything (~230 GB)
hf download GerardDMG/USEFUL --repo-type dataset --local-dir USEFUL

# Metadata + LiDAR + front camera only
hf download GerardDMG/USEFUL --repo-type dataset --local-dir USEFUL \
  --include "v1.0/*" --include "shards/LIDAR/*" --include "shards/NARROW/*"

# Only the validation split, all sensors
hf download GerardDMG/USEFUL --repo-type dataset --local-dir USEFUL \
  --include "v1.0/*" --include "shards/*/val/*" --include "shards/GPS/*" \
  --include "shards/HDG/*" --include "shards/IMU/*" --include "shards/INS/*"
```

Or from Python:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="GerardDMG/USEFUL",
    repo_type="dataset",
    local_dir="USEFUL",
    allow_patterns=["v1.0/*", "shards/LIDAR/*", "shards/NARROW/*"],
)
```

Downloads are checksummed by the Hub client and resume if interrupted.

### Extract

Every tar unpacks into `samples/<CHANNEL>/…`. Extract them from the dataset root, in parallel
and in any order:

```bash
cd USEFUL
find shards -name '*.tar' -print0 | xargs -0 -n1 -P8 tar -xf
rm -r shards   # optional, once extracted
```

## Data format

After extraction:

```text
USEFUL/                          <- dataroot
├── v1.0/                        <- metadata, 12 JSON tables
└── samples/
    ├── LIDAR/  RADAR_LEFT/  RADAR_RIGHT/                       .pcd
    ├── NARROW/  WIDE_LEFT/  WIDE_RIGHT/  LWIR/  SWIR/                 .png
    ├── POLARIMETRIC/                                           .bmp
    └── GPS/  HDG/  IMU/  INS/                                  one .csv per log
```

Sensor files are named `{vehicle}-{datetime}__{CHANNEL}__{timestamp_us}.{ext}`, for example
`u001-2024-11-19-12-46-25+0100__LIDAR__1732020526668479.pcd`.

The metadata follows the [nuScenes](https://www.nuscenes.org/) schema. Every record has a
32-character hex `token`, and tables point to each other by token:

| Table                  | One record per…                                            |
|------------------------|------------------------------------------------------------|
| `log`                  | recording session: vehicle, date, location, weather        |
| `scene`                | clip within a log: description, split, first/last sample   |
| `sample`               | synchronised multi-sensor keyframe (`prev` / `next` linked) |
| `sample_data`          | sensor file belonging to a sample                          |
| `ego_pose`             | GPS, INS, IMU and dual-antenna heading readings at a sample |
| `sensor`               | sensor channel and modality                                |
| `calibrated_sensor`    | sensor's extrinsics and intrinsics                         |
| `sample_annotation`    | 3D box of an instance in a sample                          |
| `sample_annotation_2d` | 2D box of an instance in one camera image                  |
| `instance`             | tracked object                                             |
| `category`             | object class                                               |
| `visibility`           | visibility level of a 2D box                               |

<div align="center">
<img src="https://huggingface.co/datasets/GerardDMG/USEFUL/resolve/main/media/USEFUL_database.png" width="640" alt="Relationships between the USEFUL metadata tables">
</div>

## Devkit

The [USEFUL devkit](https://github.com/GDMG99/useful-devkit) loads the tables, resolves file
paths, renders samples and scenes, exports mmdetection3d info files, and evaluates 3D detection.

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

```python
from useful import USEFUL

usfl = USEFUL(version="v1.0", dataroot="USEFUL", verbose=True)
scene = usfl.scene[0]
sample = usfl.get("sample", scene["first_sample_token"])
print(usfl.get_sample_data_path(sample["data"]["LIDAR"]))
```

## License

[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/): free for **non-commercial**
use, with attribution.

## Acknowledgements

The data format and devkit build on [nuScenes](https://github.com/nutonomy/nuscenes-devkit)
and [TruckScenes](https://github.com/TUMFTM/truckscenes-devkit).

<div align="center">
<img src="https://huggingface.co/datasets/GerardDMG/USEFUL/resolve/main/media/useful_vehicle.jpg" width="560" alt="The USEFUL recording vehicle">
</div>

## Citation

If you use USEFUL, please cite the dataset:

```bibtex
@misc{useful_dataset_2026,
  title        = {{USEFUL}: mUltimodal Sensing and pErception For autonomoUs vehicLes},
  author       = {DeMas-Giménez, Gerard and Subirana, Adrià and Bernal, Eduardo and García-Gómez, Pablo and Casas, Josep Ramon and Royo, Santiago},
  year         = {2026},
  version      = {v1.0},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/GerardDMG/USEFUL}},
  note         = {Dataset}
}
```