---
language:
- en
- ja
license: cc-by-nc-4.0
pretty_name: 360CityArena
arxiv: 2608.08814
size_categories:
- n<1K
tags:
- benchmark
- embodied-ai
- multimodal
- visual-reasoning
- navigation
- 360-degree
- unity
- image
- tabular
- datasets
---

# 360CityArena

360CityArena is a Unity-based embodied navigation and visual reasoning benchmark
for multimodal large language models. This dataset release contains 175 tasks
across seven task families and the reference images required by those tasks.

*August 11, 2026: Task 7006 was corrected after we found that it duplicated task 7009.*

- [Paper page](https://huggingface.co/papers/2608.08814)
- [Project page](https://360mm-team.github.io/360CityArena/)
- [GitHub repository](https://github.com/360MM-Team/360CityArena)

## Loading

```python
from datasets import load_dataset

dataset = load_dataset("hal-utokyo/360CityArena")
test = dataset["test"]
print(test.num_rows)  # 175
print(test[0])
```

The `reference_image` field is an image for tasks that require one and `None`
otherwise. There are 75 task rows with a reference image and 51 unique image
files: one localization map shared by 25 tasks, 25 landmark images, and 25
navigation maps.

## Task inventory

| Task family | Tasks |
| --- | ---: |
| Localization | 25 |
| Landmark Search with Language | 25 |
| Landmark Search with Image | 25 |
| Counting | 25 |
| Map Navigation | 25 |
| Language Guided Navigation | 25 |
| Relational Spatial Reasoning | 25 |

All records are in the `test` split because 360CityArena is an evaluation
benchmark, not a training corpus.

## Record fields

Each row contains the task ID and family, difficulty, initial environment index,
task prompt, ground-truth answer, and task-specific goal fields.
`reference_image_path` identifies the corresponding runner asset while
`reference_image` provides the image directly through 🤗 Datasets and the Dataset
Viewer. The legacy `source_file`, `source_row`, and `source_record` fields retain
provenance from the initial Hub migration.

## Intended use and limitations

The dataset is intended for evaluating embodied multimodal agents with the
360CityArena Unity environment and runner. Loading this dataset alone does not
run the interactive benchmark; use the code and evaluation protocol in the
GitHub repository. The benchmark covers a fixed virtual reconstruction of an
urban area and should not be treated as evidence of general navigation ability,
physical-world safety, or geographic coverage. Some reference images contain
Japanese storefront text and other scene-specific visual information.

## License and attribution

Dataset records and benchmark assets are distributed under the Creative Commons
Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0), except where
third-party terms apply. Map-derived assets include OpenStreetMap data:
copyright OpenStreetMap contributors, available under the Open Database License
(ODbL). See the repository's `DATA_LICENSE` and `NOTICE` files for details.

The benchmark runner and other source code are separately licensed under
Apache-2.0 in the GitHub repository.

## Citation

```bibtex
@inproceedings{watanabe2026360cityarena,
  title     = {360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents},
  author    = {Watanabe, Kenta and Miyai, Atsuyuki and Takenawa, Mizuki and Aizawa, Kiyoharu and Yamasaki, Toshihiko},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```