The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
title: string
description: string
version: string
row_count: int64
files: struct<data.parquet: struct<path: string>, data.geojson: struct<path: string>, data.csv: struct<path (... 10 chars omitted)
child 0, data.parquet: struct<path: string>
child 0, path: string
child 1, data.geojson: struct<path: string>
child 0, path: string
child 2, data.csv: struct<path: string>
child 0, path: string
repository: string
publisher: string
to
{'version': Value('string'), 'title': Value('string'), 'publisher': Value('string'), 'repository': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
title: string
description: string
version: string
row_count: int64
files: struct<data.parquet: struct<path: string>, data.geojson: struct<path: string>, data.csv: struct<path (... 10 chars omitted)
child 0, data.parquet: struct<path: string>
child 0, path: string
child 1, data.geojson: struct<path: string>
child 0, path: string
child 2, data.csv: struct<path: string>
child 0, path: string
repository: string
publisher: string
to
{'version': Value('string'), 'title': Value('string'), 'publisher': Value('string'), 'repository': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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 geolocated records from 2025 and is provided as a sample of a much larger dataset of mapped features.
Prepared and distributed by EmbedEarth from OpenStreetMap contributors.
Search millions more geographic features
This Hugging Face release is one downloadable dataset from the broader EmbedEarth 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 — programmable infrastructure for Earth
- Geographic Feature List — browse geographic features available through EmbedEarth
- Developer Documentation — APIs, SDKs, tools, guides, and examples
- Geographic Search SDK — 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:
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 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 from data contributed to OpenStreetMap.
OpenStreetMap data is available under the Open Database License (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 and OpenStreetMap attribution guidance.
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.
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