---
pretty_name: SpatialUAV
language:
  - en
license: other
task_categories:
  - visual-question-answering
tags:
  - uav
  - drone
  - spatial-reasoning
  - multi-view
  - aerial-ground
  - motion-understanding
  - benchmark
size_categories:
  - 1K<n<10K
configs:
  - config_name: full
    data_files:
      - split: test
        path: annotations_shuffled.parquet
  - config_name: subset_20pct_per_task
    data_files:
      - split: test
        path: annotations_subset_20pct_per_task.parquet
---

# SpatialUAV

## Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion

[![Project](https://img.shields.io/badge/Project-GitHub-181717?logo=github)](https://github.com/Hyu-Zhang/SpatialUAV)
[![Paper](https://img.shields.io/badge/arXiv-2606.27876-b31b1b.svg)](https://arxiv.org/abs/2606.27876)

SpatialUAV is a diagnostic benchmark for evaluating spatial intelligence in real low-altitude unmanned aerial vehicle (UAV) scenarios. It contains **4,331 curated visual question-answering instances across 14 task types**, covering semantic discrimination, spatial relations, aerial-aerial collaboration, aerial-ground collaboration, and motion understanding.

The benchmark provides seven visual-input configurations and nine answer formats under a unified visual-input-question-answer schema. It is intended for benchmark evaluation and contains a single `test` split.

## Dataset Configurations

| Configuration | Split | Rows | Purpose |
| --- | --- | ---: | --- |
| `full` | `test` | 4,331 | Full benchmark evaluation |
| `subset_20pct_per_task` | `test` | 863 | Faster development and human-reference subset |

Load the annotation tables with `datasets`:

```python
from datasets import load_dataset

full = load_dataset("Hyu-Zhang/SpatialUAV", "full", split="test")
subset = load_dataset(
    "Hyu-Zhang/SpatialUAV",
    "subset_20pct_per_task",
    split="test",
)
```

The Parquet files contain image paths rather than embedded image bytes. Download and extract the visual archives before resolving these paths.

## Record Structure

Each annotation contains five fields:

| Field | Type | Description |
| --- | --- | --- |
| `id` | string | Unique identifier; its prefix denotes the task |
| `image` | list[string] | Ordered paths to images or video frames |
| `conversations` | list[object] | Question or instruction in `{from, value}` format |
| `source` | string | Dataset source tag (`SpatialUAV`) |
| `GT` | string | Canonical ground-truth answer |

Example:

```json
{
  "id": "Region_Recognition_00001",
  "image": ["./SpatialUAV/samples_Single_Image/img0001.jpg"],
  "conversations": [
    {
      "from": "human",
      "value": "Which regions in the image contain a parking lot? Answer with only the region labels, formatted exactly like `Region 1, 2`. No explanation."
    }
  ],
  "source": "SpatialUAV",
  "GT": "Region 3, 4."
}
```

The full annotations are provided as `annotations.jsonl`, `annotations_shuffled.jsonl`, and the viewer-friendly `annotations_shuffled.parquet`.

## Downloading and Extracting the Visual Data

Clone the dataset repository with Git LFS:

```bash
git lfs install
git clone https://huggingface.co/datasets/Hyu-Zhang/SpatialUAV
cd SpatialUAV
```

The aerial-ground archive is distributed in multiple parts. Reassemble and verify it before extraction:

```bash
cat A2G.zip.part-* > A2G.zip
sha256sum -c A2G.zip.sha256  # Linux
# shasum -a 256 -c A2G.zip.sha256  # macOS
```

Extract the visual archives in the repository root:

```bash
unzip A2A.zip
unzip A2G.zip
unzip samples_Single_Image.zip
unzip samples_Motion_Understanding_Frames.zip
```

The resulting directories are:

```text
SpatialUAV/
├── samples_Single_Image/
├── samples_A2A_Pured/
├── samples_A2A_detected/
├── samples_A2A_Occlusion_Removal/
├── samples_A2G_Pured/
├── samples_A2G_detected/
├── samples_A2G_Path_Planning/
└── samples_Motion_Understanding_Frames/
```

Annotation paths begin with `./SpatialUAV/` and resolve when evaluation is launched from the parent directory of the cloned repository.

## More Information

For task definitions, dataset construction, evaluation metrics, model inference, and benchmark results, see the [GitHub repository](https://github.com/Hyu-Zhang/SpatialUAV) and the [SpatialUAV paper](https://arxiv.org/abs/2606.27876).

## Licensing

The **code** in the [SpatialUAV GitHub repository](https://github.com/Hyu-Zhang/SpatialUAV) is released under the MIT License. The **benchmark data is not relicensed under MIT**. It is derived from multiple source datasets and remains subject to their respective licenses and terms of use. Accordingly, this Hugging Face dataset is marked with `license: other`.

Users are responsible for reviewing and complying with the terms of BEDI, AirCopBench, MAVREC, AirScape, University-1652, and any upstream resources incorporated by those datasets.

## Citation

If you use SpatialUAV, please cite:

```bibtex
@article{zhang2026spatialuav,
  title   = {SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion},
  author  = {Zhang, Haoyu and Liu, Meng and Xiang, Qianlong and Wang, Kun and Wang, Yaowei and Nie, Liqiang},
  journal = {arXiv preprint arXiv:2606.27876},
  year    = {2026}
}
```

Please also cite the applicable source datasets when using the corresponding visual data.