README.md

---
tags:
- geospatial
- open-data
- tabular
- computer-vision
- accessibility
- pedestrian-infrastructure
- tactile-paving
- urban-planning
- mapping
- parquet
- geojson
- csv
- openstreetmap
- year-2025
libraries:
- datasets
size_categories:
- 1M<n<10M
license: odbl
pretty_name: "Tactile Paving Dataset - Global Accessibility Infrastructure Data - EmbedEarth"
---

# Tactile Paving Dataset - Global Accessibility Infrastructure Data - EmbedEarth

This dataset maps tactile paving features from OpenStreetMap. Tactile paving uses patterned or textured surfaces that can be detected underfoot or with a mobility cane, helping people identify routes, platform edges, crossings, hazards, and changes in walking surfaces.

This release contains up to **1,100,000 geolocated records** from 2025. The export is capped at 1.1 million records, so it should be understood as a large snapshot rather than a complete count of every mapped feature.

Prepared and distributed by **[EmbedEarth](https://embed.earth)** from **[OpenStreetMap contributors](https://www.openstreetmap.org/copyright)**.

## Search millions more geographic features

This Hugging Face release is one downloadable dataset from the broader **[EmbedEarth](https://embed.earth)** geographic index. EmbedEarth provides developers and AI systems with tools for searching and working with geographic features, places, infrastructure, and other observations of the physical world.

### Build with EmbedEarth

* **[EmbedEarth](https://embed.earth)** — programmable infrastructure for Earth
* **[Geographic Feature List](https://www.embed.earth/catalog/features)** — browse geographic features available through EmbedEarth
* **[Developer Documentation](https://www.embed.earth/docs)** — APIs, SDKs, tools, guides, and examples
* **[Geographic Search SDK](https://www.embed.earth/docs/sdk/search)** — search geographic features and regions programmatically
* **API** — integrate geographic search and spatial data into applications
* **SDK** — build geographic capabilities directly into applications
* **CLI** — work with geographic data from the terminal
* **MCP** — connect geographic search and spatial tools to AI agents

## Search the physical world

The same geographic infrastructure used to create this dataset can support searches such as:

```text
tactile paving near subway stations in Toronto
accessible pedestrian infrastructure in Montreal
tactile paving around schools in New York
sidewalk accessibility features in London
```

## Dataset overview

This dataset focuses on **tactile paving** represented in OpenStreetMap. Each record is a geolocated map feature with source attribution and, where available, additional tags such as names, addresses, references, operators, websites, access details, and feature-specific values.

Mapped tactile paving and detectable-surface features intended to help pedestrians, including people who are blind or have low vision, navigate crossings, platforms, sidewalks, and other walking environments.

## Use cases

### Accessibility mapping

Map tactile paving coverage around crossings, transit stops, sidewalks, and public facilities.
### Pedestrian network analysis

Study how accessible walking infrastructure connects across neighborhoods and corridors.
### Urban design research

Compare mapped accessibility infrastructure with roads, buildings, transit, and land-use data.
### OpenStreetMap quality checks

Find areas where accessibility tags are present, missing, or inconsistent.
### Geospatial machine learning

Build spatial features for accessibility research, map enrichment, or geographic AI.

## Schema

The downloadable Parquet and CSV files use a normalized schema. Source-specific attributes are preserved inside the JSON-encoded `properties` field rather than expanded into separate top-level columns. GeoJSON exposes the same record attributes alongside its geometry.

| Column | Type | Description |
| --- | --- | --- |
| `id` | string | Stable identifier for the exported record. |
| `sample` | boolean | Whether this record was selected for the optional image archive sample. |
| `latitude` | float64 | Latitude in decimal degrees using WGS 84 when a valid location is available. |
| `longitude` | float64 | Longitude in decimal degrees using WGS 84 when a valid location is available. |
| `geometry_wkb` | binary | The record geometry encoded as Well-Known Binary for spatial workflows. |
| `media_url` | string | URL for associated imagery or other visual media when available. |
| `attribution` | string | Attribution information carried into the exported record. |
| `source` | string | Source or provider associated with the observation. |
| `properties` | string | JSON-encoded object containing source-specific OpenStreetMap attributes. |

### Source-specific properties

The `properties` field preserves additional OpenStreetMap tags associated with each feature. Exact keys vary by record and region; common examples include:

| Property | Description |
| --- | --- |
| `osm_id` | OpenStreetMap object identifier when supplied. |
| `name / ref` | Name, reference, or local identifier when mapped. |
| `surface / tactile_paving` | Surface or tactile-paving tag values when supplied. |
| `access / wheelchair` | Accessibility-related tags when mapped. |
| `operator / website` | Responsible organization or public information link when supplied. |
| `address` | Address or location text when available. |

Not every property is populated for every record.

## Download

The dataset is available in Parquet, GeoJSON, and CSV formats:

* [Parquet](https://huggingface.co/datasets/EmbedEarth/tactile-paving/resolve/main/data.parquet)
* [GeoJSON](https://huggingface.co/datasets/EmbedEarth/tactile-paving/resolve/main/data.geojson)
* [CSV](https://huggingface.co/datasets/EmbedEarth/tactile-paving/resolve/main/data.csv)

**Parquet** is recommended for analytics, Python workflows, DuckDB, and large-scale processing. **GeoJSON** is useful for GIS software and web maps. **CSV** is convenient for tabular analysis and interoperability.

## Data sources and attribution

This dataset was prepared and distributed by **[EmbedEarth](https://embed.earth)** from data contributed to **[OpenStreetMap](https://www.openstreetmap.org/copyright)**.

OpenStreetMap data is available under the **[Open Database License (ODbL) 1.0](https://opendatacommons.org/licenses/odbl/1.0/)**. When using or redistributing the data, retain the OpenStreetMap attribution and follow the applicable ODbL requirements. Record-level media or third-party links may have additional terms set by their original providers.

Suggested attribution:

> Contains information from OpenStreetMap, which is made available under the Open Database License (ODbL). https://www.openstreetmap.org/copyright

## Methodology and limitations

Records were exported from an OpenStreetMap snapshot for 2025. The dataset represents mapped feature coverage, not a complete audit of accessibility conditions. A missing record does not prove that tactile paving is absent, and a mapped record does not independently verify the current condition or installation quality.

OpenStreetMap coverage and tagging vary by place and contributor. Geographic absence should not be interpreted as real-world absence, and mapped presence should not be treated as an independent inspection or operational certification.

## License

The OpenStreetMap-derived database in this repository is made available under the **Open Database License (ODbL) 1.0**. See the [ODbL license text](https://opendatacommons.org/licenses/odbl/1.0/) and [OpenStreetMap attribution guidance](https://www.openstreetmap.org/copyright).

EmbedEarth-created explanatory text and metadata are provided to help users understand the release. Users are responsible for complying with the terms applying to OpenStreetMap data and any underlying third-party media or links.