---
tags:
- geospatial
- open-data
- tabular
- energy-infrastructure
- utilities
- power-grid
- infrastructure
- mapping
- parquet
- geojson
- csv
- openstreetmap
- year-2025
libraries:
- datasets
size_categories:
- 1M<n<10M
license: odbl
pretty_name: "Power Tower Dataset - Global Electrical Infrastructure Data - EmbedEarth"
---

# Power Tower Dataset - Global Electrical Infrastructure Data - EmbedEarth

This dataset maps power towers from OpenStreetMap. Power towers are structures used to carry overhead electric lines, typically along transmission or distribution corridors. The dataset is useful for understanding how energy infrastructure is represented geographically and for combining utility context with other spatial layers.

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
power towers near airports in Texas
transmission infrastructure in California
power corridors around Toronto
utility infrastructure near industrial zones in Chicago
```

## Dataset overview

This dataset focuses on **power towers** 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 towers and structures that support overhead electric power lines, including features tagged as man_made=power_tower in openstreetmap.

## Use cases

### Energy infrastructure mapping

Map overhead transmission structures and explore the geography of power corridors.
### Utility-corridor analysis

Combine tower locations with lines, roads, land use, buildings, and environmental constraints.
### Infrastructure planning

Support exploratory studies of proximity, access, development, and resilience around power assets.
### Geospatial machine learning

Create spatial features for infrastructure detection, map enrichment, or geographic AI.
### OpenStreetMap quality checks

Analyze coverage and consistency of power-infrastructure tags across regions.

## 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. |
| `power` | Power feature classification when mapped. |
| `operator / owner` | Operator or owner information when supplied. |
| `ref` | Asset or corridor reference when available. |
| `voltage / cables` | Electrical or line-related tags when mapped. |
| `name / location` | Name or location information when supplied. |

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/power-tower/resolve/main/data.parquet)
* [GeoJSON](https://huggingface.co/datasets/EmbedEarth/power-tower/resolve/main/data.geojson)
* [CSV](https://huggingface.co/datasets/EmbedEarth/power-tower/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 map reflects available community-maintained data and may omit assets, contain positional uncertainty, or use local tagging conventions. It is not an authoritative inventory of the electric grid and should not be used alone for engineering, safety, or regulatory decisions.

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.