Datasets:
license: cc-by-4.0
pretty_name: Outerview Global Road Signs Index
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
tags:
- computer-vision
- image-classification
- geospatial
- mapping
- earth-observation
- transportation
- navigation
- infrastructure
- road-signs
- traffic-signs
- autonomous-driving
- street-view
- open-data
annotations_creators:
- Paul Wynter
source_datasets:
- original
language:
- en
size_categories:
- 10K<n<100K
Outerview Global Road Signs Index
A large-scale geospatial index of road signs and traffic signage with latitude and longitude.
This dataset is part of Outerview’s mission to organize the world’s physical infrastructure and make it searchable, measurable, and continuously updated.
🌍 Overview
- Feature: Road signs and traffic signage
- Scope: Global
- Entries: 15,000
- Total Index: Millions of locations (full system)
- Formats: Parquet / CSV / GeoJSON
This index represents a subset of a much larger global system tracking road infrastructure and signage across the world.
Each row corresponds to a real-world road sign observed at a specific location.
🌎 Why This Index Exists
Road signs are a critical layer of navigation, safety, and infrastructure intelligence.
This index is designed to support a continuous stream of real-world data, enabling:
- Tracking changes to road signage over time
- Detecting newly added, removed, or damaged signs
- Improving navigation systems and map accuracy
- Supporting autonomous driving and routing systems
- Monitoring infrastructure consistency across regions
Our goal is simple:
maintain a living, global index of road signals that reflects how roads evolve in real time.
📊 Index Schema
| Column | Description |
|---|---|
| id | Unique identifier |
| latitude | Latitude coordinate |
| longitude | Longitude coordinate |
| region | Administrative region |
| source | Data source |
🧠 Data Sources & Labeling
- Imagery Source: Mapillary
- Index Generation: Outerview AI models
All detections were generated using Outerview’s computer vision systems trained on large-scale real-world imagery.
🧪 Example Use Cases
- Train models for traffic sign recognition
- Improve navigation and routing systems
- Support autonomous vehicle perception systems
- Monitor road infrastructure changes over time
- Build geospatial search and world modeling systems
🚀 About Outerview
Outerview is a research lab focused on building world models that help understand and index the physical world.
Our system is trained on billions of images, videos, and location data, enabling anyone to search and analyze real-world conditions globally.
🔗 API & Full Index Access
This is a sample index.
The full platform provides:
- Millions of additional locations
- Temporal data (track changes over time)
- Access to billions of real-world images and video streams
- Real-time querying of physical infrastructure and navigation signals
Access the full index and API:
👉 https://outerview.ai
View API documentation:
👉 https://outerview.ai/developers/docs
🔄 Updates
This index is actively maintained and updated biweekly with new observations and improvements.
💬 Feedback
We welcome feedback from researchers, developers, and transportation experts.
If there are additional features you'd like to see (e.g. lane markings, signals, road conditions), let us know.
📜 License
This index is released under the CC-BY-4.0 license.
Free for research and commercial use with attribution.
⚠️ Notes
- This index is a sampled subset of a larger global system
- The full index includes additional metadata such as timestamps
- Coverage and density may vary by region
- Detection accuracy may vary depending on environment and conditions