---
annotations_creators:
- human-annotated
language_creators:
- found
language: en
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset'
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-classification
- text-retrieval
task_ids:
- multi-class-image-classification
tags:
- Place Recognition
---

# MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset

**Multimodal Street-Level Visual Place Recognition Dataset (MMS-VPR)** is a novel, open-access dataset designed to advance research in visual place recognition (VPR) and multimodal urban scene understanding. This dataset focuses on complex, fine-grained, and pedestrian-only urban environments, addressing a significant gap in existing VPR datasets that often rely on vehicle-based imagery from road networks and overlook dense, walkable spaces—especially in non-Western urban contexts.

The dataset was collected within a ~70,800 m² open-air commercial district in Chengdu, China, and consists of:

- **747** smartphone-recorded videos (1Hz frame extraction),
- **1,417** manually captured images,
- **78,581** total images and frames annotated with **207** unique place classes (e.g., street segments, intersections, square).

Each media file includes:

- Precise **GPS metadata** (latitude, longitude, altitude),
- Fine-grained **timestamps**,
- **Human-verified annotations** for class consistency.

The data was collected via a systematic and replicable protocol with multiple camera orientations (north, south, east, west) and spans a full daily cycle from **7:00 AM to 10:00 PM**, ensuring diversity in lighting and temporal context (day and night).

The spatial layout of the dataset forms a natural **graph structure** with:

- 61 horizontal edges (street segments),
- 64 vertical edges,
- 81 nodes (intersections),
- 1 central square (subgraph).

This makes the dataset suitable for **graph-based learning tasks** such as GNN-based reasoning, and for multimodal, spatiotemporal, and structure-aware inference.

We also provide two targeted subsets:

- **Sub-Dataset_Edges** (125 classes): horizontal and vertical street segments.
- **Sub-Dataset_Points** (82 classes): intersections and the square.

This dataset demonstrates that **high-quality Place Recognition datasets** can be constructed using **widely available smartphones** when paired with a scientifically designed data collection framework—lowering the barrier to entry for future dataset development.

## Dataset Structure

The dataset is organized into three main folders:

### 01. Raw_Files (approximately 90 GB, 2,164 files)

This folder contains the original raw data collected in an urban district. It includes:

- `Photos/`: Over 1,400 high-resolution photographs
- `Videos/`: Over 700 videos recorded using handheld mobile cameras

These are unprocessed, original media files. Resolutions include:
- Image: 4032 × 3024
- Video: 1920 × 1080

### 02. Annotated_Original (approximately 38 GB, 162,186 files)

This folder contains the annotated version of the dataset. Videos have been sampled at 1 frame per second (1 Hz), and all images and video frames are manually labeled with place tags and descriptive metadata.

Subfolders:
- `Dataset_Full/`: The complete version of the dataset
- `Sub-Dataset_Edges/`: A subset containing only edge spaces (street segments)
- `Sub-Dataset_Points/`: A subset containing node spaces (intersections) and squares

Each dataset variant contains three modalities:
- `Images/`: High-resolution image files and video frames
- `Videos/`: High-resolution video clips
- `Texts/`: Text files containing annotations and metadata

Subfolder structure in `Images/` and `Videos/` in `Dataset_Full/`: 
- `Edge (horizontal)`
- `Edge (vertical)`
- `Node`
- `Square`

Subfolder structure in `Images/` and `Videos/` in `Dataset_Edges/`: 

- `Edge (horizontal)`
- `Edge (vertical)`

Subfolder structure in `Images/` and `Videos/` in `Dataset_Points/`: 

- `Node`
- `Square`

Each of these contains multiple subfolders named after spatial location codes (e.g., `Eh-1-1`, `N-2-3`), which correspond to place labels used for classification. These labels can be mapped to numeric indices.

Text files in `Texts/`:
- `Annotations.xlsx`: Place labels, spatial types, map locations, shop names, signage text, and label indices
- `Media_Metadata-Images.xlsx`: Metadata for each image
- `Media_Metadata-Videos.xlsx`: Metadata for each video

In addition, each dataset variant contains a visualization map (e.g., `Dataset_Full Map.png` in`Dataset_Full/`) demonstrating the real-world geolocations and relations between all the places in the target urban district, which also indicates the inherent graph structure of the proposed dataset.

### 03. Annotated_Resized (approximately 4 GB, 162,186 files)

This is a downscaled version of the annotated dataset, identical in structure to Annotated_Original. All image and video frame resolutions are reduced:

- Original images (4032×3024) are resized to 256×192
- Video frames (1920×1080) are resized to 256×144

Aspect ratios are preserved. This version is recommended for faster training and experimentation.

## File Download and Reconstruction

Due to Hugging Face's file size limits, the dataset is split into multiple compressed files:

- `Raw_Files.part01.tar.gz`  
- `Raw_Files.part02.tar.gz`  
- `Raw_Files.part03.tar.gz`  
- `Annotated_Original.tar.gz`  
- `Annotated_Resized.tar.gz`

To reconstruct the raw files:

```bash
cat Raw_Files.part*.tar.gz > Raw_Files.tar.gz
tar -xzvf Raw_Files.tar.gz
```

## Usage

```bash
# Download the resized version (recommended)
wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Annotated_Resized.tar.gz
tar -xzvf Annotated_Resized.tar.gz
```

Researchers can directly train models using the contents of `Dataset_Full`, which includes aligned image, text, and video modalities. For efficient training, the resized version is usually sufficient. For high-fidelity testing or custom processing, use the full-resolution version or raw files.

## Dataset Summary

| Dataset Version     | Size       | File Count |
|---------------------|------------|-------------|
| Raw Files           | ~90 GB     | 2,164 |
| Annotated_Original  | ~38 GB     | 162,186     |
| Annotated_Resized   | ~4 GB   | 162,186     |

## License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

## Citation

If you use this dataset in your research, please cite:

```bibtex
@article{ou2025mmsvpr,
  title     = {MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset and Benchmark},
  author    = {Ou, Yiwei and Ren, Xiaobin and Sun, Ronggui and Gao, Guansong and Jiang, Ziyi and Zhao, Kaiqi and Manfredini, Manfredo},
  journal   = {arXiv preprint arXiv:2505.12254},
  year      = {2025},
  url       = {https://arxiv.org/abs/2505.12254}
}