---
license: cc-by-4.0
task_categories:
- image-to-text
- visual-question-answering
- image-classification
language:
- zh
- en
tags:
- poi
- points-of-interest
- street-view
- satellite
- urban
- multimodal
- vision-language
- benchmark
pretty_name: Multi-City POI Multimodal Benchmark
size_categories:
- 1M<n<10M
---

# POI Benchmark: Multi-City Multimodal Points of Interest

A large-scale multimodal benchmark pairing **Points of Interest (POIs)** with **street-view imagery**, **aerial grid photos**, and **satellite imagery** across **10 major cities** on 3 continents.

## Cities

Beijing, Chengdu, Guangzhou, Hong Kong, Shanghai, Shenzhen, London, Melbourne, New York, Sydney.

## Contents

| Path | Size | Type | Description |
|---|---|---|---|
| `metadata_aligned.tar` | 8.6 GB | 11 JSON files | Enriched & aligned POI metadata per city (one JSON per city + index) |
| `poi_data.tar` | 30 GB | JSONL / JSON | Raw POI text records and evaluation queries |
| `grid_images/<city>.tar` | ~111 GB total | JPG | Shop-front / storefront grid images |
| `streetview_grids/<city>.tar` | ~402 GB total | JPG | Street-view panorama grids |
| `streetview_sampled/<city>.tar` | ~1.4 TB total | JPG | Higher-resolution street-view grids (~500m sampling) |
| `merged_satellite/<city>.tar` | ~1.5 TB total | PNG | Merged multi-tile satellite images |

**Total: ~3.4 TB across ~2.6M images + structured POI records.**

## Archive format

All image collections are packaged as **uncompressed `.tar`** archives (one per city per modality). Images are already JPG/PNG — additional compression would not reduce size.

Each tar contains a top-level directory matching the city name, e.g. inside `grid_images/beijing.tar`:

```
beijing/
  beijing_0_grid.jpg
  beijing_1_grid.jpg
  ...
```

## Load with `datasets`

```python
from huggingface_hub import hf_hub_download
import tarfile, json

# 1. Metadata
path = hf_hub_download(
    repo_id="sukiewang/poi-benchmark",
    filename="metadata_aligned.tar",
    repo_type="dataset",
)
with tarfile.open(path) as t:
    t.extractall("./metadata")

# 2. One city's grid images
path = hf_hub_download(
    repo_id="sukiewang/poi-benchmark",
    filename="grid_images/beijing.tar",
    repo_type="dataset",
)
```

Or stream directly with **WebDataset**:

```python
import webdataset as wds
url = "https://huggingface.co/datasets/sukiewang/poi-benchmark/resolve/main/grid_images/beijing.tar"
ds = wds.WebDataset(url).decode("pil")
for sample in ds:
    print(sample["__key__"], sample["jpg"].size)
    break
```

## Intended use

- POI recognition / retrieval from visual context
- Vision-language grounding (match POI text to streetview / satellite)
- Urban scene understanding
- Cross-city generalization benchmarks

## Citation

```bibtex
@misc{wang2026poibench,
  title  = {Multi-City POI Multimodal Benchmark},
  author = {Wang, Sukie},
  year   = {2026},
  url    = {https://huggingface.co/datasets/sukiewang/poi-benchmark},
}
```

## License

Released under **CC-BY-4.0**. Third-party imagery (satellite tiles, street-view) remains under the original providers' terms; this release aggregates data for research use only.