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

Overview

MMS-VPR is the first large-scale multimodal street-level visual place recognition dataset featuring comprehensive integration of images, videos, and rich textual annotations with day-night coverage and 7-year temporal span in dense pedestrian-only environments.

Keywords: Visual Place Recognition, Multimodal Learning, Pedestrian Navigation, Urban Computing, Graph Neural Networks, Day-Night VPR, Street-Level Localization

Key Features

• 🚶 Pedestrian-Only Perspective: First VPR dataset systematically collected in dense pedestrian commercial districts
• 🌓 Day-Night Coverage: Balanced temporal sampling across daytime (7AM-5PM) and nighttime (6PM-10PM)
• 🎯 Multimodal: Images + Videos + Text annotations (GPS, store names, spatial metrics)
• 📅 7-Year Temporal Span: Field collection (2024) + Social media data (2019-2025)
• 🗺️ Graph Structure: 208 locations organized in spatial graph with connectivity relationships
• 🏙️ Urban Science Integration: Space syntax metrics enriching spatial configuration context

Dataset Statistics

Modality	Count/Coverage	Source	Details
Images	110,529 images	Field + Social Media	Resolution: 256×192 (preprocessed)
Videos	2,527 clips	Field Collection	20-60s, 256×144, 30fps
Texts	208 locations	OCR + Manual	GPS, store names, signage, spatial metrics
Graph Structure	208 nodes/edges	Urban Network	Connectivity, distances, space syntax
Locations	208 unique places	Chengdu Taikoo Li	~70,800 m² pedestrian district
Temporal Coverage	7 years	2019-2025	Fine-grained + long-term span

Spatial Composition:

• 81 Nodes (street intersections)
• 125 Edges (61 horizontal + 64 vertical streets)
• 2 Squares (large open spaces)

---

Visual Overview

Dataset Framework

Our dataset addresses four critical limitations in existing VPR datasets through systematic multimodal data collection:

Figure 1: MMS-VPR framework addressing four VPR limitations: (1) pedestrian-only perspective, (2) day-night coverage, (3) multimodal integration (images + videos + text), and (4) 7-year temporal span enhanced with social media data.

Data Collection Pipeline

Figure 2: Systematic methodology—site collection → data processing → textual annotation → social media integration

Benchmark Platform

Figure 3: MMS-VPRlib unified platform integrating datasets, models, and evaluation pipelines. GitHub Repository

---

Dataset Structure

The dataset comprises four main components:

MMS-VPR/
├── Images/                        # 110,529 images across 208 location folders
│   ├── N-1-1/                     # Node (intersection) images
│   ├── Eh-1-1/                    # Horizontal edge (street) images
│   ├── Ev-1-1/                    # Vertical edge (street) images
│   └── S-1/                       # Square images
├── Videos/                        # 2,527 video clips
│   ├── N-1-1/                     # Videos organized by location
│   └── ...
├── Texts/                         # Textual annotations and metadata
│   ├── Annotations.xlsx           # Location labels, GPS, store names, signage
│   ├── Metadata-Images.xlsx       # Image EXIF metadata
│   └── Metadata-Videos.xlsx       # Video metadata
└── Graph Structure/               # Spatial graph organization
    ├── Graph_Structure_README.md  # Detailed graph documentation
    ├── 00 Street Network Graph.pdf
    ├── 01 Node Features.xlsx
    ├── 02 Edge Features.xlsx
    ├── 03 Edge Connections.xlsx
    └── 04 Square Features.xlsx

Location Encoding System

Each location follows a hierarchical encoding scheme:

• Nodes (Intersections): N-i-j where i=row, j=column (e.g., N-1-1, N-2-3)
• Horizontal Edges (Streets): Eh-i-j for east-west streets (e.g., Eh-1-1)
• Vertical Edges (Streets): Ev-j-i for north-south streets (e.g., Ev-1-1)
• Squares: S-k ranked by area (e.g., S-1 is the largest)

This encoding preserves both geometric position and topological relationships, enabling graph-based learning.

Multimodal Data Composition

1. Image Data (110,529 images)

Field Collection (78,575 images):

• Systematic coverage from 4 cardinal directions (N, S, E, W)
• Dual perspectives: horizontal (0°) + upward (45°)
• Day-night balanced sampling (7AM-10PM)
• 1Hz frame extraction from videos + standalone photos

Social Media (31,954 images):

• Curated from Weibo (2019-2025)
• Georeferenced to 208 locations
• Diverse viewpoints and 7-year temporal extent

Resolution: 256×192 (preprocessed), original 4032×3024 available upon request

2. Video Data (2,527 clips)

• Duration: 20-60 seconds per clip
• Resolution: 256×144 @ 30fps (preprocessed), original 1920×1080
• Captured along streets from multiple directions
• Day-night coverage enabling motion-aware place recognition

3. Textual Annotations

Each location includes:

Spatial Identifiers:

• Systematic codes preserving graph topology (e.g., N-3-4, Eh-5-2)
• Enable adjacency matrix construction for GNN applications

Semantic Text:

• Store names and visible signage (OCR-extracted + manually verified)
• Example: "Starbucks, Adidas, LEGO, MUJI"
• Supports text-based retrieval and multimodal fusion

Geospatial Data:

• GPS coordinates (longitude, latitude)
• Altitude information from EXIF metadata

Space Syntax Metrics (from urban science):

• Integration: Global accessibility measure
• Betweenness: Through-movement potential
• Computed using both angular and weighted distances
• See Graph_Structure_README.md for details

Graph-Based Organization

All 208 locations form a spatial graph G = (V, E) representing the pedestrian network:

• Nodes (V): 81 street intersections + 2 squares
• Edges (E): 125 pedestrian street segments
• Attributes: Physical properties, connectivity, turning angles, distances

The Graph Structure/ folder provides complete:

• Network visualization (PDF)
• Node/edge feature tables
• Connection tables with angular/Euclidean distances
• Ready-to-use adjacency matrices for GNN research

For detailed graph documentation, see Graph_Structure_README.md

---

Download and Usage

Note on Dataset Viewer: This dataset is distributed as compressed archives (.tar.gz) for efficient storage and distribution. The Hugging Face Dataset Viewer is not available for this format, which is standard for large-scale image datasets (similar to ImageNet, Places365). This does not affect dataset usage.

Quick Start

The dataset is split into four components for flexible downloading (~11GB total):

# Download all components
wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Images.tar.gz
wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Videos.tar.gz
wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Texts.tar.gz
wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Graph_Structure.tar.gz

# Extract all
tar -xzf Images.tar.gz
tar -xzf Videos.tar.gz
tar -xzf Texts.tar.gz
tar -xzf Graph_Structure.tar.gz

# Dataset is ready to use!

Selective Download (Save Bandwidth)

Download only what you need:

For image-only experiments (~2.3GB):

wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Images.tar.gz
wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Texts.tar.gz
tar -xzf Images.tar.gz
tar -xzf Texts.tar.gz

For video-based research (~8.8GB):

wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Videos.tar.gz
wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Texts.tar.gz
tar -xzf Videos.tar.gz
tar -xzf Texts.tar.gz

For graph-based methods (minimal):

wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Graph_Structure.tar.gz
wget https://huggingface.co/datasets/Yiwei-Ou/MMS-VPR/resolve/main/Texts.tar.gz
tar -xzf Graph_Structure.tar.gz
tar -xzf Texts.tar.gz

File Breakdown

Component	Size	Contents	Use Case
Images.tar.gz	2.25 GB	110,529 images (256×192)	Image-based VPR
Videos.tar.gz	8.78 GB	2,527 videos (256×144)	Video-based VPR, motion analysis
Texts.tar.gz	417 KB	Annotations, metadata	Multimodal learning, text features
Graph_Structure.tar.gz	112 KB	Spatial graph, topology	GNN-based methods, spatial analysis
Total	~11 GB	Complete multimodal dataset	All experiments

Using Hugging Face Hub (Alternative)

from huggingface_hub import hf_hub_download

# Download specific components
images_path = hf_hub_download(
    repo_id="Yiwei-Ou/MMS-VPR",
    filename="Images.tar.gz",
    repo_type="dataset"
)

texts_path = hf_hub_download(
    repo_id="Yiwei-Ou/MMS-VPR",
    filename="Texts.tar.gz",
    repo_type="dataset"
)

After Extraction

Your directory structure will be:

MMS-VPR/
├── Images/           # 208 location folders with images
├── Videos/           # 208 location folders with video clips
├── Texts/            # Annotation files (xlsx format)
└── Graph Structure/  # Network topology and features

Original High-Resolution Data

The preprocessed dataset uses reduced resolution for efficiency. Original high-resolution data (~120GB+) is available upon reasonable request:

• Images: 4032×3024 (original camera resolution)
• Videos: 1920×1080 @ 30fps

Please contact the authors via GitHub issues or email for access to original data.

---

Data Collection Methodology

Site Selection

Location: Chengdu Taikoo Li, Chengdu, China

• Area: ~70,800 m²
• Type: Open-air commercial district (pedestrian-only)
• Characteristics: Dense retail, dining, leisure, cultural spaces

Collection Principles

Our methodology addresses three critical VPR challenges:

1. Four-Direction Coverage

• Systematic capture from N, S, E, W directions
• Addresses viewpoint variation (40% performance drop when viewpoints differ)
• Superior to 360° panoramas (avoid geometric distortions)

2. Dual-Perspective Capture

• Horizontal (0°): Eye-level navigation features
• Upward (45°): Building facades and landmarks
• Matches human visual field strategies in urban environments

3. Balanced Day-Night Sampling

• Daytime: 7AM-5PM
• Nighttime: 6PM-10PM
• Equal data volume for illumination-robust learning

Equipment

• Consumer smartphones: iPhone XS Max, iPhone 11 Pro Max
• Accessible and reproducible: No specialized equipment required
• Framework designed for replication in other cities

Pipeline

1. Site Collection: Systematic field surveys (2024)
2. Data Processing: Resolution standardization, frame extraction, cleaning
3. Textual Annotation: GPS, store names, OCR signage, spatial metrics
4. Social Media Integration: Weibo data (2019-2025) for temporal span

---

Use Cases

Visual Place Recognition

• Street-level localization in pedestrian environments
• Cross-temporal place matching (day-night, seasonal)
• Viewpoint-invariant recognition

Multimodal Learning

• Vision-language models for place recognition
• Video-based motion-aware VPR
• Text-guided visual retrieval

Graph Neural Networks

• Spatial relationship modeling
• GNN-based place recognition
• Graph-constrained retrieval

Urban Analytics

• Pedestrian flow analysis
• Spatial accessibility studies
• Commercial district characterization

Augmented Reality & Navigation

• Pedestrian AR applications
• Indoor-outdoor transition scenarios
• Context-aware wayfinding

---

Benchmark Platform

MMS-VPRlib: Unified benchmarking platform consolidating:

• Multiple datasets (Pittsburgh, Tokyo 24/7, Nordland, MMS-VPR)
• 17+ baseline models (CNN, RNN, Transformer, multimodal)
• Standardized evaluation pipeline
• Modular components for data processing, modeling, fusion

Repository: https://github.com/yiasun/MMS-VPRlib

---

Privacy and Ethics

All data collection adheres to ACM and KDD ethical guidelines:

Privacy Protection

• ✅ All faces and license plates automatically detected and pixelated
• ✅ Manual review to verify PII removal
• ✅ Public outdoor spaces only (no private/restricted areas)
• ✅ Preprocessed low-resolution version (256×144) further protects privacy

Ethical Data Collection

• ✅ Pedestrian-level viewpoints (natural eye-level, not surveillance)
• ✅ Public accessible areas during normal hours (7AM-10PM)
• ✅ No intrusion during late-night hours
• ✅ Systematic, unbiased spatial and temporal coverage

Note: While automated anonymization has been applied, if you identify any privacy concerns, please contact us immediately.

---

License

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

You are free to:

• Share: Copy and redistribute the material
• Adapt: Remix, transform, and build upon the material
• Commercial use: Use for any purpose, including commercially

Under the following terms:

• Attribution: You must give appropriate credit and indicate if changes were made

---

Acknowledgments

We thank all contributors to this dataset. Field data were collected by the research team in 2024. Social media data were curated from publicly shared posts on Weibo (2019-2025).

---

Citation

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

@misc{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 Zhao, Kaiqi and Manfredini, Manfredo},
  year         = {2025},
  eprint       = {2505.12254},
  archivePrefix= {arXiv},
  primaryClass = {cs.CV},
  url          = {https://arxiv.org/abs/2505.12254}
}

---

Contact

For questions, issues, or requests for original high-resolution data:

• GitHub Issues: MMS-VPR Repository
• Email: you661@aucklanduni.ac.nz
• Hugging Face: Dataset page

---

Updates

Latest Version: v2.0 (February 2026)

• 208 locations (updated from 207)
• Integrated social media data (2019-2025)
• Enhanced textual annotations
• Complete graph structure documentation